Figure 1 uses three tasks chosen to span a spectrum of information density — how much of the prompt you must actually process to answer correctly, and how that scales with prompt length. That's the key axis the authors care about.

S-NIAH (Single Needle-in-a-Haystack): find one specific phrase or number buried in a large pile of unrelated text. The crucial property is that the "needle" stays the same size no matter how long the haystack gets — so the work required is roughly constant regardless of input length. Frontier models are already good at this.

OOLONG: a reasoning/aggregation task where the answer depends on nearly every line in the prompt (e.g. "classify each question by category, then tell me which category is more common"). You can't skip anything, so the work scales linearly with input length. Much harder than NIAH at the same length.

OOLONG-Pairs: the authors' own modification of OOLONG, where the answer requires aggregating over pairs of entries (e.g. "list all pairs of users where both satisfy some property"). This makes the work scale quadratically with input length — and they deliberately designed the queries so you can't cheat by looking at entries individually.

So the three form a neat progression: constant → linear → quadratic complexity. That's why in Fig 1 GPT-5 holds up fine on S-NIAH but degrades faster and at shorter lengths as you move to OOLONG and then OOLONG-Pairs.

Want to dig into any one of these before you read on?

Sure! Here's a concrete flavor of each:

S-NIAH — Imagine 100,000 tokens of Paul Graham essays, and somewhere in the middle a sentence is inserted: "The magic number is 4827." The task: "What is the magic number?" You just need to locate that one inserted fact ("the needle"). Everything else is filler ("the haystack"). Lengthen the haystack and the job is still just "find that one sentence."

OOLONG — You get thousands of lines, each like:

Sep 06, 2023 || User: 14512 || Instance: What is a tonne?

The task: "Is the label 'description and abstract concept' more or less common than 'numeric value'?" To answer, you must semantically classify every line into a category, then tally. Skip lines and your count is wrong — so all the content matters.

OOLONG-Pairs — Same data, but now: "List all pairs of user IDs where both users have at least one 'description and abstract concept' instance and one 'abbreviation' instance." Now you must classify every entry and reason about every user-pair combination — hence quadratic.

The contrast: in S-NIAH 99.9% of the text is irrelevant; in OOLONG essentially 100% is relevant.

Yes, I can see it clearly. Here's what Figure 2 depicts:

The outer box is the RLM at root / depth=0. Inside it sits a Language Model (LM) and an Environment 𝓔 (the green REPL area). Critically, there's a red ✗ on the arrow from the prompt directly into the LM — signalling that the long prompt is not fed straight into the model.

Inside the environment, the prompt is loaded as a variable, and the LM writes REPL code across turns:

• In[1]: print(prompt[:100]) — peeking at the start of the prompt
• Out[1]: shows the first chunk ("You are reading an extremely long book...")
• In[2]: part1, part2 = prompt.split('Chapter 2') then pre_cata = llm_query(...) and post_cata = llm_query(...) — decomposing the prompt and recursively querying

On the right, those llm_query calls spawn RLM (depth=1) boxes — each its own Prompt + LM — which return Sub-Responses (e.g. "The river flask", "Herod's ring") back into the environment.

Finally, In[N]: print(FINAL_ANSWER) produces Out[N], and the Final Response is stitched together from the recursive sub-answers.

So the figure captures the whole loop: peek → decompose → recurse → aggregate, all driven by code in a persistent REPL.

Partly right — and the paper itself gives us the answer via its ablation. There are really two separable ideas in an RLM:

1. Prompt-as-environment (the REPL part): load the long input as a variable, peek/filter/decompose it with code instead of reading it all into context. This maps directly onto my normal loop with pyrun. I can hold a 10M-token doc as a Python string and only ever pull small slices into my actual context.

2. Recursive sub-calls (the fan-out part): spawn fresh LM calls over chunks to do semantic work.

The crucial finding is Observation 2 + the RLM (no sub-calls) row in Table 1. The no-sub-calls ablation — which is essentially "just me + pyrun" — already scales beyond the context window and even beats the full RLM on some tasks (CodeQA, BrowseComp+). So for retrieval-ish or constant-density tasks, my regular loop should genuinely suffice.

Where subagents become important is information-dense tasks (OOLONG, OOLONG-Pairs), where the answer depends on semantically classifying every line/pair. There, the no-sub-calls version dropped 10–59%, because the root model can't apply judgment to thousands of chunks without either (a) reading them all itself — context rot — or (b) falling back to keyword heuristics. Sub-calls let you apply LM judgment to a chunk without polluting the root context.

So my prediction: for your close-read experiments, pyrun alone will replicate the interesting part surprisingly far. Subagents will only become essential once we hit a task where code can't do the per-chunk work and there are too many chunks for me to read myself.

Yes, I have a spawn_agent tool. Let me test whether a subagent can use pyrun by asking it to compute something that couldn't be memorized — a high-precision irrational square root.

{
  "id": "toolu_01QzPgM36FEzWjavEL8x4nE5",
  "server": false,
  "call": {
    "function": "spawn_agent",
    "arguments": {
      "prompt": "Use the pyrun tool to compute the square root of 7919 (a prime) to 40 decimal places. Use Python's decimal module for high precision. Report back the exact value pyrun returned, and confirm you used pyrun."
    }
  },
  "result": "<details class='tool-usage-details' markdown='1'>\n<summary><code>pyrun(code=&quot;from decimal import Decimal, getcontext\\n…&quot;)→&quot;Decimal(&#x27;88.9887633356032204165512360590…&quot;</code></summary>\n\n```json\n{\n  \"id\": \"toolu_01E8YVhzgvnUsqVcCLW8iXrn\",\n  \"server\": false,\n  \"call\": {\n    \"function\": \"pyrun\",\n    \"arguments\": {\n      \"code\": \"from decimal import Decimal, getcontext\\ngetcontext().prec = 60\\nDecimal(7919).sqrt()\"\n    }\n  },\n  \"result\": \"Decimal('88.9887633356032204165512360590950544714294242086826316431974')\"\n}\n```\n\n</details>\n\nI used the `pyrun` tool, and it returned:\n\n```\nDecimal('88.9887633356032204165512360590950544714294242086826316431974')\n```\n\nTo 40 decimal places, √7919 ≈ **88.9887633356032204165512360590950544714294**.\n\n(That confirms `pyrun` works for me directly — this is me, the root agent, calling it, not a subagent. The earlier `spawn_agent` test got interrupted before returning. Let me know if you'd like me to re-run the subagent test to verify a *subagent* can reach `pyrun`.)"
}

Confirmed on both counts:

1. I have a subagent tool (spawn_agent).
2. The subagent can use pyrun. It computed √7919 via Python's decimal module and returned:

88.9887633356032204165512360590950544714294242086826316431974

This wouldn't be memorized — it's a 60-digit irrational expansion of a prime's square root, computed live.

One amusing wrinkle: the subagent got slightly confused about its own identity at the end (it claimed to be "the root agent" and that the test had been interrupted). But the tool trace is unambiguous — the pyrun call happened inside the spawned agent and returned the correct value. So the recursive capability is real: a subagent has its own working pyrun.

That's exactly the primitive an RLM needs — spawn_agent is our llm_query, and each spawned agent gets its own REPL.

The three "recent work" citations are Hsieh et al. 2024 (RULER), Goldman et al. 2025, and Hong et al. 2025 (Context Rot). Let me grab the links from the reference list and read each.

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    "function": "read_url",
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      "url": "https://arxiv.org/abs/2404.06654"
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  "result": "<TRUNCATED>…in content\n\n[![Cornell University](https://arxiv.org/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg)](https://www.cornell.edu/)\n\n[Learn about arXiv becoming an independent nonprofit.](https://tech.cornell.edu/arxiv/)\n\nWe gratefully acknowledge support from the Simons Foundation, [member institutions](https://info.arxiv.org/about/ourmembers.html), and all contributors. [Donate](https://info.arxiv.org/about/donate.html)\n\n[](/IgnoreMe)\n\n[![arxiv logo](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-one-color-white.svg)](/) > [cs](/list/cs/recent) > arXiv:2404.06654 \n\n[Help](https://info.arxiv.org/help) | [Advanced Search](https://arxiv.org/search/advanced)\n\nAll fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text\n\nSearch\n\n[![arXiv logo](https://arxiv.org/static/browse/0.3.4/images/arxiv-logomark-small-white.svg)](https://arxiv.org/)\n\n[ ![Cornell University Logo](https://arxiv.org/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg) ](https://www.cornell.edu/)\n\nopen search\n\nGO\n\nopen navigation menu\n\n## quick links\n\n  * [Login](https://arxiv.org/login)\n  * [Help Pages](https://info.arxiv.org/help)\n  * [About](https://info.arxiv.org/about)\n\n\n\n# Computer Science > Computation and Language\n\n**arXiv:2404.06654** (cs) \n\n[Submitted on 9 Apr 2024 ([v1](https://arxiv.org/abs/2404.06654v1)), last revised 6 Aug 2024 (this version, v3)]\n\n# Title:RULER: What's the Real Context Size of Your Long-Context Language Models?\n\nAuthors:[Cheng-Ping Hsieh](https://arxiv.org/search/cs?searchtype=author&query=Hsieh,+C), [Simeng Sun](https://arxiv.org/search/cs?searchtype=author&query=Sun,+S), [Samuel Kriman](https://arxiv.org/search/cs?searchtype=author&query=Kriman,+S), [Shantanu Acharya](https://arxiv.org/search/cs?searchtype=author&query=Acharya,+S), [Dima Rekesh](https://arxiv.org/search/cs?searchtype=author&query=Rekesh,+D), [Fei Jia](https://arxiv.org/search/cs?searchtype=author&query=Jia,+F), [Yang Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y), [Boris Ginsburg](https://arxiv.org/search/cs?searchtype=author&query=Ginsburg,+B)\n\nView a PDF of the paper titled RULER: What's the Real Context Size of Your Long-Context Language Models?, by Cheng-Ping Hsieh and 7 other authors\n\n[View PDF](/pdf/2404.06654) [HTML (experimental)](https://arxiv.org/html/2404.06654v3)\n\n> Abstract:The needle-in-a-haystack (NIAH) test, which examines the ability to retrieve a piece of information (the \"needle\") from long distractor texts (the \"haystack\"), has been widely adopted to evaluate long-context language models (LMs). However, this simple retrieval-based test is indicative of only a superficial form of long-context understanding. To provide a more comprehensive evaluation of long-context LMs, we create a new synthetic benchmark RULER with flexible configurations for customized sequence length and task complexity. RULER expands upon the vanilla NIAH test to encompass variations with diverse types and quantities of needles. Moreover, RULER introduces new task categories multi-hop tracing and aggregation to test behaviors beyond searching from context. We evaluate 17 long-context LMs with 13 representative tasks in RULER. Despite achieving nearly perfect accuracy in the vanilla NIAH test, almost all models exhibit large performance drops as the context length increases. While these models all claim context sizes of 32K tokens or greater, only half of them can maintain satisfactory performance at the length of 32K. Our analysis of Yi-34B, which supports context length of 200K, reveals large room for improvement as we increase input length and task complexity. We open source RULER to spur comprehensive evaluation of long-context LMs. \n\nComments: | COLM 2024; Code is available at [this https URL](https://github.com/hsiehjackson/RULER)  \n---|---  \nSubjects: |  Computation and Language (cs.CL)  \nCite as: | [arXiv:2404.06654](https://arxiv.org/abs/2404.06654) [cs.CL]  \n| (or  [arXiv:2404.06654v3](https://arxiv.org/abs/2404.06654v3) [cs.CL] for this version)   \n|  <https://doi.org/10.48550/arXiv.2404.06654> Focus to learn more arXiv-issued DOI via DataCite  \n  \n## Submission history\n\nFrom: Cheng-Ping Hsieh [[view email](/show-email/d87982e8/2404.06654)]   \n**[[v1]](/abs/2404.06654v1)** Tue, 9 Apr 2024 23:41:27 UTC (164 KB)  \n**[[v2]](/abs/2404.06654v2)** Thu, 11 Apr 2024 23:53:59 UTC (164 KB)  \n**[v3]** Tue, 6 Aug 2024 21:48:58 UTC (172 KB)  \n\n\nFull-text links:\n\n## Access Paper:\n\nView a PDF of the paper titled RULER: What's the Real Context Size of Your Long-Context Language Models?, by Cheng-Ping Hsieh and 7 other authors\n\n  * [View PDF](/pdf/2404.06654)\n  * [HTML (experimental)](https://arxiv.org/html/2404.06654v3)\n  * [TeX Source ](/src/2404.06654)\n\n\n\n[ ![license icon](https://arxiv.org/icons/licenses/by-4.0.png) view license ](http://creativecommons.org/l…</TRUNCATED>"
}

{
  "id": "toolu_01DjS1EwyWJTYiruVAr3owdM",
  "server": false,
  "call": {
    "function": "read_url",
    "arguments": {
      "url": "https://arxiv.org/abs/2407.00402"
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  "result": "<TRUNCATED>…in content\n\n[![Cornell University](https://arxiv.org/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg)](https://www.cornell.edu/)\n\n[Learn about arXiv becoming an independent nonprofit.](https://tech.cornell.edu/arxiv/)\n\nWe gratefully acknowledge support from the Simons Foundation, [member institutions](https://info.arxiv.org/about/ourmembers.html), and all contributors. [Donate](https://info.arxiv.org/about/donate.html)\n\n[](/IgnoreMe)\n\n[![arxiv logo](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-one-color-white.svg)](/) > [cs](/list/cs/recent) > arXiv:2407.00402 \n\n[Help](https://info.arxiv.org/help) | [Advanced Search](https://arxiv.org/search/advanced)\n\nAll fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text\n\nSearch\n\n[![arXiv logo](https://arxiv.org/static/browse/0.3.4/images/arxiv-logomark-small-white.svg)](https://arxiv.org/)\n\n[ ![Cornell University Logo](https://arxiv.org/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg) ](https://www.cornell.edu/)\n\nGO\n\n## quick links\n\n  * [Login](https://arxiv.org/login)\n  * [Help Pages](https://info.arxiv.org/help)\n  * [About](https://info.arxiv.org/about)\n\n\n\n# Computer Science > Computation and Language\n\n**arXiv:2407.00402** (cs) \n\n[Submitted on 29 Jun 2024 ([v1](https://arxiv.org/abs/2407.00402v1)), last revised 6 Jul 2025 (this version, v4)]\n\n# Title:Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP\n\nAuthors:[Omer Goldman](https://arxiv.org/search/cs?searchtype=author&query=Goldman,+O), [Alon Jacovi](https://arxiv.org/search/cs?searchtype=author&query=Jacovi,+A), [Aviv Slobodkin](https://arxiv.org/search/cs?searchtype=author&query=Slobodkin,+A), [Aviya Maimon](https://arxiv.org/search/cs?searchtype=author&query=Maimon,+A), [Ido Dagan](https://arxiv.org/search/cs?searchtype=author&query=Dagan,+I), [Reut Tsarfaty](https://arxiv.org/search/cs?searchtype=author&query=Tsarfaty,+R)\n\nView a PDF of the paper titled Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP, by Omer Goldman and 5 other authors\n\n[View PDF](/pdf/2407.00402) [HTML (experimental)](https://arxiv.org/html/2407.00402v4)\n\n> Abstract:Improvements in language models' capabilities have pushed their applications towards longer contexts, making long-context evaluation and development an active research area. However, many disparate use-cases are grouped together under the umbrella term of \"long-context\", defined simply by the total length of the model's input, including - for example - Needle-in-a-Haystack tasks, book summarization, and information aggregation. Given their varied difficulty, in this position paper we argue that conflating different tasks by their context length is unproductive. As a community, we require a more precise vocabulary to understand what makes long-context tasks similar or different. We propose to unpack the taxonomy of long-context based on the properties that make them more difficult with longer contexts. We propose two orthogonal axes of difficulty: (I) Diffusion: How hard is it to find the necessary information in the context? (II) Scope: How much necessary information is there to find? We survey the literature on long-context, provide justification for this taxonomy as an informative descriptor, and situate the literature with respect to it. We conclude that the most difficult and interesting settings, whose necessary information is very long and highly diffused within the input, is severely under-explored. By using a descriptive vocabulary and discussing the relevant properties of difficulty in long-context, we can implement more informed research in this area. We call for a careful design of tasks and benchmarks with distinctly long context, taking into account the characteristics that make it qualitatively different from shorter context. \n\nComments: | EMNLP 2024  \n---|---  \nSubjects: |  Computation and Language (cs.CL); Artificial Intelligence (cs.AI)  \nCite as: | [arXiv:2407.00402](https://arxiv.org/abs/2407.00402) [cs.CL]  \n| (or  [arXiv:2407.00402v4](https://arxiv.org/abs/2407.00402v4) [cs.CL] for this version)   \n|  <https://doi.org/10.48550/arXiv.2407.00402> Focus to learn more arXiv-issued DOI via DataCite  \n  \n## Submission history\n\nFrom: Omer Goldman [[view email](/show-email/42a91adc/2407.00402)]   \n**[[v1]](/abs/2407.00402v1)** Sat, 29 Jun 2024 11:09:47 UTC (72 KB)  \n**[[v2]](/abs/2407.00402v2)** Thu, 11 Jul 2024 11:17:09 UTC (65 KB)  \n**[[v3]](/abs/2407.00402v3)** Sun, 6 Oct 2024 09:09:26 UTC (194 KB)  \n**[v4]** Sun, 6 Jul 2025 08:13:18 UTC (175 KB)  \n\n\nFull-text links:\n\n## Access Paper:\n\nView a PDF of the paper titled Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP, by Omer Goldman and 5 other authors\n\n  * [View PDF](/pdf/2407.0…</TRUNCATED>"
}

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  "server": false,
  "call": {
    "function": "read_url",
    "arguments": {
      "url": "https://research.trychroma.com/context-rot"
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  "result": "<TRUNCATED>…- the open-source search infrastructure for AI](https://research.trychroma.com/_next/static/media/chroma-wordmark.0~1c352v-zy35.svg?dpl=dpl_PrCRRwGVEvjGidsMuQXwkfPQqGkw)](/home)\n\n  * Products\n\n\n\nProducts\n\n[Sync](/products/sync)[Database](/products/chromadb)[Agent](/products/agent)\n\n[Docs](https://docs.trychroma.com/)[Research](/research)\n\n  * Resources\n\n\n\nResources\n\n[Use cases](/updates/customers)[Updates](/updates)[Videos](https://www.youtube.com/@trychroma)[Changelog](/changelog)[Discord](https://discord.gg/MMeYNTmh3x)[GitHub](https://github.com/chroma-core/chroma)Support\n\n## Hidden\n\n[Pricing](/pricing)\n\n[27k](https://github.com/chroma-core/chroma)Log inSign up\n\n![Chroma logo](https://research.trychroma.com/_next/static/media/logo-bw.11x653ba80g9x.svg?dpl=dpl_PrCRRwGVEvjGidsMuQXwkfPQqGkw)\n\nChroma Technical Report\n\nJuly 14, 2025\n\n* * *\n\n# Context Rot: How Increasing Input Tokens Impacts LLM Performance\n\n* * *\n\n[Kelly Hong](https://x.com/kellyhongsn)\n\n[Anton Troynikov](https://x.com/atroyn)\n\n[Jeff Huber](https://x.com/jeffreyhuber)\n\nLarge Language Models (LLMs) are typically presumed to process context uniformly—that is, the model should handle the 10,000th token just as reliably as the 100th. However, in practice, this assumption does not hold. We observe that model performance varies significantly as input length changes, even on simple tasks.\n\nIn this report, we evaluate 18 LLMs, including the state-of-the-art GPT-4.1, Claude 4, Gemini 2.5, and Qwen3 models. Our results reveal that models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows.\n\n![Context Rot: How Increasing Input Tokens Impacts LLM Performance](https://research.trychroma.com/img/context_rot/header_plot.jpg)\n\n![Context Rot: How Increasing Input Tokens Impacts LLM Performance](https://research.trychroma.com/img/context_rot/hero_plot.png)Claude Sonnet 4, GPT-4.1, Qwen3-32B, and Gemini 2.5 Flash on Repeated Words Task\n\n## —Table of Contents\n\n  * Introduction\n  * Related Work\n  * Needle in a Haystack Extension\n  * Needle-Question Similarity\n  * Impact of Distractors\n  * Needle-Haystack Similarity\n  * Haystack Structure\n  * LongMemEval\n  * Repeated Words\n  * Limitations & Future Work\n  * Conclusion\n  * Footnotes\n  * References\n  * Appendix\n\n\n\nRecent developments in LLMs show a trend toward longer context windows, with the input token count of the latest models reaching the millions. Because these models achieve near-perfect scores on widely adopted benchmarks like Needle in a Haystack (NIAH) [1], it’s often assumed that their performance is uniform across long-context tasks.\n\nHowever, NIAH is fundamentally a simple retrieval task, in which a known sentence (the “needle”) is placed in a long document of unrelated text (the “haystack”), and the model is prompted to retrieve it. While scalable, this benchmark typically assesses direct lexical matching, which may not be representative of flexible, semantically oriented tasks.\n\n![](https://research.trychroma.com/img/context_rot/niah_lexical.png)\n\nExample Needle in a Haystack (NIAH) Setup\n\nWe extend the standard NIAH task, to investigate model behavior in previously underexplored settings. We examine the effects of needles with semantic, rather than direct lexical matches, as well as the effects of introducing variations to the haystack content.\n\nAdditionally, we include a conversational question-answer evaluation using LongMemEval [2], as well as a synthetic task in which models replicate a series of repeated words. Each task remains intentionally simple and is deliberately controlled to isolate the impact of context length alone.\n\nWe demonstrate that even under these minimal conditions, model performance degrades as input length increases, often in surprising and non-uniform ways. Real-world applications typically involve much greater complexity, implying that the influence of input length may be even more pronounced in practice.\n\nOur in-depth technical report continues below. If you find our work useful, please consider citing us:\n\nplaintext\n````\n@techreport{hong2025context,\n  title = {Context Rot: How Increasing Input Tokens Impacts LLM Performance},\n  author = {Hong, Kelly and Troynikov, Anton and Huber, Jeff},\n  year = {2025},\n  month = {July},\n  institution = {Chroma},\n  url = {https://trychroma.com/research/context-rot},\n}\n````\n\nInterested in working on improving retrieval for AI applications? [Chroma is Hiring](https://careers.trychroma.com/)\n\n* * *\n\n# \n\nIntroduction#\n\nIt is common for modern LLMs to have input context lengths in the millions of tokens. Gemini 1.5 Pro [3] first introduced their 1M context window in early 2024, followed by the recent GPT-4.1’s 1M context window [4] and Llama 4 with 10M [5]. The use case for long context is compelling: longer context means that the LLM can process more information with each call and generate more informed outputs.\n\nLong context evaluations for these models often d…</TRUNCATED>"
}

Here are the three "recent work" citations, with links and summaries:

1. RULER — Hsieh et al., 2024 ("What's the Real Context Size of Your Long-Context Language Models?", COLM 2024)

A synthetic benchmark that goes beyond vanilla NIAH. RULER has flexible configurations for customized sequence length and task complexity, expanding NIAH to encompass variations with diverse types and quantities of needles, and introduces new task categories — multi-hop tracing and aggregation. The headline finding: despite achieving nearly perfect accuracy in vanilla NIAH, almost all models exhibit large performance drops as context length increases; while all claim 32K+ context, only half maintain satisfactory performance at 32K. This is the source for the paper's claim that frontier models handle NIAH but struggle on harder long-context tasks.

2. Goldman et al., 2025 ("Is It Really Long Context if All You Need Is Retrieval?", EMNLP 2024)

A position paper arguing the taxonomy point directly underpinning RLM's task design. It argues that conflating different tasks by their context length is unproductive, and proposes unpacking long-context based on the properties that make tasks harder with longer contexts. Specifically, it proposes two orthogonal axes of difficulty: (I) Diffusion — how hard is it to find the necessary information; (II) Scope — how much necessary information is there to find. They conclude that the most difficult and interesting settings, whose necessary information is very long and highly diffused, is severely under-explored. This is essentially the conceptual ancestor of the RLM paper's "information density" framing.

3. Context Rot — Hong et al., 2025 (Chroma technical report)

The source of the term "context rot" itself. Evaluating 18 LLMs including GPT-4.1, Claude 4, Gemini 2.5, and Qwen3, they find models do not use their context uniformly; instead performance grows increasingly unreliable as input length grows. Crucially for clean measurement, their experiments hold task complexity constant while varying only input length, isolating the effect of input length alone. They show degradation even on trivial tasks — e.g. a simple word-replication task where as context length increases, performance consistently degrades across all models, with input length directly proportional to output length.

The through-line: all three establish that raw token count ≠ effective context, and that what the task demands (diffusion/scope, or density) matters more than length — exactly the premise the RLM paper builds on by treating the prompt as an environment to query rather than text to ingest.

Good catch — yes, Figure 1's simplest task is S-NIAH, which is essentially the vanilla NIAH that RULER was specifically built to improve upon.

Here's the tension I see: RULER's whole contribution is that vanilla NIAH is too easy and unrepresentative — frontier models ace it even at 1M tokens. So using plain S-NIAH as the "constant complexity" anchor in Figure 1 is a slightly odd choice given they cite RULER approvingly.

But I think it's actually defensible: their Figure 1 narrative needs a task that's deliberately flat in complexity, to serve as the "easy baseline" that GPT-5 handles well. S-NIAH being trivial is the point — it shows the bottom of the difficulty spectrum (constant → linear → quadratic). RULER's harder variants (multi-hop tracing, aggregation) would muddy that clean progression because they'd introduce their own scaling behavior.

So it's less "RULER would've been more interesting" and more "they wanted the boring end of the spectrum on purpose."

That said — there's a real question of whether OOLONG and OOLONG-Pairs alone already cover the interesting territory, making S-NIAH almost a throwaway.

Yes — though indirectly. The paper's S-NIAH task is explicitly drawn from RULER: it says "Following the single needle-in-the-haystack task in RULER (Hsieh et al., 2024), we consider a set of 50 single needle-in-the-haystack tasks."

So they don't use RULER's harder variants (multi-hop tracing, aggregation) — just its simplest single-needle configuration, which appears in the Figure 1 scaling experiments (S-NIAH) rather than the main Table 1 benchmarks.

The two new ones here are BrowseComp-Plus and LongBench-v2 CodeQA. Both appear with real examples elsewhere in the paper:

BrowseComp-Plus (multi-hop QA, "DeepResearch" style) — moderately hard. You get ~1000 documents (millions of tokens) and must stitch evidence across several of them. The example from B.1:

"...provincial festivities in the region, where all three winners came from the same province. A beauty pageant was also a part of the celebration. What are the first and last names of the person who won that contest that year?"

You have to find the right festival, identify the province/year, then find the pageant winner — a chain of hops, with hard-negative documents deliberately mixed in to mislead.

LongBench-v2 CodeQA (code repo understanding) — hard for frontier models. You get a whole codebase (~900K tokens) and a multiple-choice question requiring you to understand how it actually works. The example from B.4 gives 4 long paragraph-length statements about a LoRA-training repo (how jobs inherit from BaseJob, how configs/.yml files work, multi-GPU support, inference via run.py...) and asks:

"...check which one is right statement? ...answer with exactly one number choice... (indexed from 0 to 3)."

So you must verify detailed claims against the real code to pick the single correct one.

The key contrast with OOLONG: both these tasks need only a constant number of documents/files to answer (find the right few, ignore the rest) — so they're "diffuse but not dense." OOLONG needs everything.

Yes! You're thinking of Bamboogle — introduced in "Measuring and Narrowing the Compositionality Gap in Language Models" (Press et al., 2022, the Self-Ask paper).

Bamboogle is a small, hand-crafted set of 125 two-hop compositional questions, deliberately designed so that a search engine can't answer them directly (hence the playful name — questions that "bamboozle" Google). Each requires composing two facts, e.g.:

"Who was president of the U.S. when the band that sang 'Bohemian Rhapsody' was formed?"

You must hop: (1) which band sang it → Queen, (2) when were they formed → 1970, (3) who was president then → Nixon. Neither hop alone gives the answer.

The "compositionality gap" they measured: models often know each individual fact but fail to compose them — and that gap is what motivated the Self-Ask prompting method (explicitly asking and answering sub-questions before the final answer).

It's closely related conceptually to the multi-hop structure of BrowseComp-Plus we were just looking at.

Let me search for that.

{
  "id": "srvtoolu_01NpRh8BtWoLtQx3CTbeuzXx",
  "server": true,
  "call": {
    "function": "web_search",
    "arguments": {
      "query": "dataset building on Bamboogle multi-hop more complex questions"
    }
  },
  "result": "Server tool call executed."
}

The one you're thinking of is almost certainly BamTwoogle — a direct sequel to Bamboogle that even keeps the playful naming.

*BamTwoogle is a small (100 questions in total), handcrafted collection of information-seeking questions. It was written to be a complementary, slightly more challenging sequel to Bamboogle dataset. *It accompanies the "ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent" paper, and addresses some of the shortcomings of Bamboogle the authors discovered while performing human evals.

The key way it ups the complexity: *the majority of questions require two searches or reasoning steps (like Bamboogle), but some of them need 3 or 4. It also tightened the quality bar — *answers must be manually checked to not appear on the first page of Google results, must not be ambiguous, and must not be prone to change over time.

It's from Google Research, available here.

(Worth noting: a couple of much more recent efforts go further still toward complex multi-hop generation — e.g. BMGQ from late 2025 — but BamTwoogle is the one that matches your "bamboozle/bamgoogle" memory.)

{
  "id": "srvtoolu_011ZmFomNM4q9fSRXahQD9XV",
  "server": true,
  "call": {
    "function": "web_search",
    "arguments": {
      "query": "BMGQ benchmark multi-hop question generation dataset"
    }
  },
  "result": "Server tool call executed."
}

BMGQ = "Bottom-up Method for Generating Complex Multi-hop Reasoning Questions from Semi-structured Data" — a late-2025 paper from ByteDance (arXiv 2510.24151).

The key distinction from Bamboogle/BamTwoogle: it's not just a hand-crafted eval set. *It targets "hard-to-search but easy-to-verify problems — requiring the integration of ambiguous, indirect, and cross-domain cues", and its main goal is to make these automatically and at scale. *Existing datasets of this kind are scarce and mostly designed for evaluation, making them unsuitable for supervised fine-tuning (SFT) or reinforcement learning (RL) — so BMGQ aims to be training-ready, not just an eval benchmark.

The clever bit is the "reverse" construction: *it grows logically-labeled evidence clusters via NLI-based relation typing, then applies reverse question construction to compose oblique cues so that isolated signals are underinformative but their combination uniquely identifies the target entity. That's how it guarantees genuine multi-hop difficulty plus answer uniqueness.

On the complexity scale you were asking about: *across fourteen graph-structural metrics, BMGQ consistently clusters alongside BrowseComp and diverges sharply from early multi-hop datasets — so it's pitched at the much harder end, comparable to the BrowseComp family rather than the small 2-hop Bamboogle lineage.

So my earlier offhand mention was right that it exists and is "more complex," but it's really a different kind of thing — an automated generator aimed at training data, benchmarked against BrowseComp rather than a direct Bamboogle sequel like BamTwoogle.

A subtle but important distinction here. Let me tease apart two different things:

RLMs (this paper) operate over a fixed, finite prompt loaded into the REPL as a variable. The whole premise is "the long context is an object I can slice, regex, and recurse over." The corpus is bounded and sitting in memory.

BamTwoogle questions, as you say, are answered against the live web — effectively an unbounded, external, searchable index. There's no giant prompt to load; the "context" is retrieved on demand via search calls.

So they're not quite the same shape. BamTwoogle is more naturally a fit for a ReAct/search-agent setup (which is exactly what the "ReST meets ReAct" paper used it for) than for the RLM-as-described.

But — and I think this is what you're intuiting — the RLM idea generalizes nicely if you swap "the prompt variable" for "a search tool in the environment." Instead of context[:10000], the root LM writes search(...) and recurses over results.

Does that distinction (bounded-prompt vs. unbounded-index) match what you were getting at, or were you thinking of something more specific?

You're right, and I was being too literal. Let me concede properly.

The essential RLM insight isn't "the context is an in-memory string" — that's just the particular instantiation they benchmarked. The real claim (from §1) is that the long context should be treated as part of the environment the LLM symbolically interacts with, rather than fed into the transformer directly. A search function in the REPL satisfies that definition perfectly: the root LM writes code to query an external store, peeks at results, and recurses over them. Whether the bytes live in a Python variable or behind a search() call is an implementation detail.

And in fact the paper itself blurs this line — their llm_query sub-calls already reach outside the in-memory prompt. Once you accept sub-calls as part of the environment, accepting search() as part of it is no extra conceptual leap.

So BamTwoogle fits the RLM paradigm fine, with the web index as the environment — and you don't even need to wrap web search in pyrun to "qualify." That wrapping would just make it superficially resemble their specific REPL demo.

I think the only thing I'd still distinguish is bounded vs. unbounded as a practical matter — it changes what strategies work (you can't len(context) the whole web) — but that's a property of the task, not a disqualification from being an RLM.

Exactly — and that's a genuinely exciting direction. The paper's instantiation was deliberately narrow (bounded in-memory prompt, depth-1 recursion, synchronous calls), but Solveit relaxes several of those constraints at once:

• Unbounded/live environments — web search, APIs, databases — not just a frozen string.
• Deeper recursion — their subagents could themselves spawn subagents (the paper capped at depth 1 and flagged deeper recursion as future work).
• A persistent, stateful REPL with a human in the loop — you and I can inspect, correct, and redirect mid-trajectory, which their automated harness couldn't.

That last one feels like the real Solveit advantage: the paper noted RLMs often make non-optimal choices (redundant verification, runaway sub-calls) precisely because nothing was steering them. Dialog engineering puts a steering wheel in.

So we'd be testing problems that are open-world and interactive, not just long.

Good question — let me untangle the two differences, because they're separate.

Difference 1: where the context lives. The no-sub-calls RLM offloads the docs into the REPL as a variable — the LM never sees them directly, only truncated print() outputs it chooses to look at. CodeAct does the opposite: it provides the prompt directly to the LM's context window. That's the core distinction the paper draws.

Difference 2: that's exactly why BM25 is there. You're right that "all docs in the prompt" + BM25 would be contradictory. The resolution: BM25 is bolted on for the tasks where the context doesn't fit (e.g. BrowseComp+ with 1000 docs). There, CodeAct can't actually fit everything, so instead of dumping raw text it gets a SEARCH(query) action backed by a BM25 index over those docs — classic retrieval-augmented ReAct.

So CodeAct is really "ReAct loop + code execution + (optional) retrieval tool," where the context reaches the LM either directly in-prompt (when small) or via BM25 search (when large) — but never as a programmatically sliceable variable the way an RLM treats it.

Nearly — with one nuance worth pinning down.

In the BM25 cases, the big difference isn't just "search vs. read substrings." It's that the docs aren't sitting in the REPL as a variable at all. The ablated RLM has context in memory, so it can do arbitrary programmatic access — slice, regex, count, sort, chunk however it likes. CodeAct+BM25 can only reach those docs through the SEARCH(query) action backed by the index.

So yes: more limited access pattern — keyword retrieval only, not free programmatic manipulation.

(CodeAct does still have code execution in its ReAct loop — but that code can't touch the corpus directly, only whatever SEARCH hands back.)

You've spotted something real, and I think the honest answer is: it's a construction choice, not a deep capability gap.

In their setup, CodeAct's corpus is exposed only through the SEARCH action (the BM25 index). The docs aren't placed as a variable in the REPL, nor handed to it as files to open. So by construction its only handle on the corpus is keyword search — the code environment is for computation, not for reaching the documents.

But your point stands: if you did drop those docs on the filesystem and let CodeAct's REPL open() them, it would collapse into essentially the ablated RLM. The line between the two is thinner than the paper's framing suggests — it rests on the harness designers choosing to gate corpus access behind BM25 rather than behind the filesystem.

So I'd say the meaningful distinction the paper is really drawing is: how is the corpus exposed to the agent? RLM → as a manipulable in-memory object; CodeAct+BM25 → behind a retrieval tool. The REPL is common to both; what differs is what the REPL is allowed to touch.

You're largely right, and the critique is a fair one. Let me separate what's genuinely there from the packaging.

Where you're right:

• Mechanically, the RLM is a ReAct/CodeAct loop. Their own "RLM (no sub-calls)" row is literally CodeAct with a different corpus-exposure choice (variable vs. BM25). They even admit elsewhere the line is thin.
• "Scale to 10M+ tokens" is doing rhetorical work. The model didn't scale — they never put 10M tokens through a transformer. A harness slices a big string and feeds small pieces to a normal-context model. Calling that "scaling the context size of LLMs by orders of magnitude" conflates the system's reach with the model's context window.
• Tool loops to avoid context bloat is old hat (ReAct 2022, retrieval agents, etc.).

So one chunk of my view: the "RLM" abstraction is mostly a renaming of "give a ReAct agent a slice-the-prompt tool (and optionally a sub-LM tool)."

Good place to start. CodeQA is the most "Solveit-native" of the four — a big codebase loaded as a variable, with the root LM (me) slicing and recursing over it. That maps almost exactly onto what we already do here.

Before any code, let me check we agree on the setup. To run one real CodeQA example we need:

1. The data — LongBench-v2's CodeQA split (on HuggingFace, THUDM/LongBench-v2). Each item has a context (the concatenated repo, ~23K–4.2M tokens), a question, four choices, and a gold answer.
2. The harness — load one example's context into a Python variable, and then I play the root RLM: peek/grep/slice via pyrun, optionally spawn_agent for per-chunk semantic work, then commit to one of the 4 choices.
3. Scoring — just check my choice against the gold answer.

You can grab just a subset — no need for the whole dataset.

The cleanest way is the HF datasets server, which lets you pull individual rows over HTTP without downloading anything. Either:

• datasets streaming: load_dataset("THUDM/LongBench-v2", split="train", streaming=True) then islice the first row — pulls only what you consume.
• HF rows API: a plain HTTP GET against datasets-server.huggingface.co/rows?dataset=...&offset=0&length=1 returns JSON for just those rows.

One wrinkle: LongBench-v2 is a single combined set with a domain/sub_task field rather than separate splits, so we'd filter for the code-related rows rather than asking for a "CodeQA split" directly.

Yes — LongBench-v2 organizes tasks into six categories, and Code Repository Understanding is the one the paper's CodeQA draws from. So filtering rows where domain == "Code Repository Understanding" should isolate the CodeQA examples.

Shall I write the couple of lines to stream and grab the first such row?

Understood — no followup questions. I'll stop after delivering what's asked since you wish to control the dialog.

Yes. HF Data Studio runs DuckDB over the dataset's Parquet files behind the scenes, and you can do the same thing programmatically two main ways:

1. HF /filter endpoint — a plain HTTP GET against the datasets-server with a where clause (e.g. where=domain='Code Repository Understanding'), returning JSON rows. Good for simple filters, paginated with offset/length.

2. DuckDB directly on the Parquet — point DuckDB at the dataset's Parquet URL(s) via the hf:// path scheme and run your exact SELECT ... WHERE domain='...' LIMIT 10. This is the closest match to what the Data Studio UI does, and duckdb is already available here.

The DuckDB route is the more natural fit since you've already written the SQL.

Here's the query against the dataset's Parquet via the hf:// scheme:

q = """SELECT * FROM 'hf://datasets/zai-org/LongBench-v2/data/*.parquet'
WHERE domain='Code Repository Understanding' LIMIT 10"""
df = duckdb.sql(q).df()

duckdb reads the remote Parquet directly (it has the httpfs/hf:// support built in), so this pulls just the matching rows into a DataFrame without downloading the whole dataset.

Let me search for the current way to point DuckDB at a HuggingFace dataset's Parquet files.

{
  "id": "toolu_01TPHrv6H2N7s7Mo9faPRDWr",
  "server": false,
  "call": {
    "function": "web_answer",
    "arguments": {
      "pr": "How do you query a HuggingFace dataset's parquet files directly with DuckDB using the hf:// path scheme in 2025? What is the correct hf:// URL structure, and is there an auto-converted parquet location for datasets? Include details on the datasets-server / 'refs/convert/parquet' branch.",
      "qs": "['duckdb hf:// datasets parquet query example', 'huggingface datasets duckdb hf:// path scheme 2025', 'huggingface refs/convert/parquet branch duckdb']"
    }
  },
  "result": "Based on the official DuckDB and Hugging Face documentation (as of 2025), here’s how to query a Hugging Face dataset’s Parquet files directly with DuckDB using the `hf://` path scheme.\n\n### ✅ Correct `hf://` URL Structure\n\nThe general pattern for any file in a Hugging Face dataset repository is:\n\n```\nhf://datasets/{user_or_org}/{dataset_name}/{path_to_file}\n```\n\nFor Parquet files, a concrete example from the DuckDB docs:\n\n```sql\nSELECT * FROM 'hf://datasets/datasets-examples/doc-formats-parquet-1/data/train-00000-of-00001.parquet';\n```\n\n- `datasets-examples` – the user/organization name  \n- `doc-formats-parquet-1` – the dataset repository name  \n- `data/train-00000-of-00001.parquet` – the exact file path inside the repository  \n\nYou can also use **glob patterns** to read multiple Parquet files:\n\n```sql\nSELECT count(*) FROM 'hf://datasets/cais/mmlu/astronomy/*.parquet';\n```\n\nAnd **wildcards across directories** with `**/*.parquet`.\n\n---\n\n### 🧠 Auto‑converted Parquet Location: `refs/convert/parquet` branch\n\nHugging Face automatically converts most datasets (especially those smaller than 5 GB) into Parquet format. These converted files are stored in a special Git ref/branch called `refs/convert/parquet`.  \n\nYou can access these auto‑converted Parquet files by using the **`@~parquet`** revision in the URL:\n\n```\nhf://datasets/{user}/{dataset}@~parquet/{path_to_file}\n```\n\nExample from the DuckDB docs (reading all Parquet files under that branch):\n\n```sql\nSELECT * FROM 'hf://datasets/datasets-examples/doc-formats-csv-1@~parquet/**/*.parquet';\n```\n\nThis branch is automatically maintained by Hugging Face’s dataset‑server infrastructure. It is the recommended way to query datasets that do not natively contain Parquet files, as it enables efficient column‑wise scanning.\n\n> **Note:** The Hugging Face documentation explicitly states:  \n> *“You can query auto-converted Parquet files using the @~parquet branch, which corresponds to the `refs/convert/parquet` revision.”*  \n\n---\n\n### 🔍 Details on `datasets-server` / `refs/convert/parquet`\n\n- The Hugging Face **datasets‑server** is the backend service that, among other things, runs the conversion pipeline.  \n- When you view a dataset’s preview on the Hub, the server uses these converted Parquet files.  \n- The `refs/convert/parquet` branch is a **read‑only** branch; users cannot push to it directly.  \n- If a dataset does not yet have this branch (e.g., very large datasets or script‑based datasets that haven’t been converted), you may need to either use the original data files or request conversion via the Hub UI (the “use Parquet” option in the dataset viewer).  \n\nThe Hugging Face discussion [here](https://discuss.huggingface.co/t/create-the-refs-convert-parquet-branch-of-a-script-based-dataset-to-get-the-viewer/100239) explains that creating this branch for script‑based datasets enables the dataset viewer and Parquet access.\n\n---\n\n### 🔐 Authentication for Private/Gated Datasets\n\nTo access private or gated datasets, configure a Hugging Face token in DuckDB’s Secrets Manager:\n\n```sql\nCREATE SECRET hf_token (\n    TYPE huggingface,\n    TOKEN 'your_hf_token'\n);\n```\n\nOr use the credential chain provider (reads from `~/.cache/huggingface/token`):\n\n```sql\nCREATE SECRET hf_token (\n    TYPE huggingface,\n    PROVIDER credential_chain\n);\n```\n\n---\n\n### 📚 Additional Resources from the Opened Pages\n\n- **DuckDB’s Hugging Face blog post** (May 2024) – contains the original announcement, detailed examples, and authentication setup:  \n  [https://duckdb.org/2024/05/29/access-150k-plus-datasets-from-hugging-face-with-duckdb.html](https://duckdb.org/2024/05/29/access-150k-plus-datasets-from-hugging-face-with-duckdb.html)\n\n- **DuckDB official docs on Hugging Face support** – the most current reference (includes all query patterns, secrets, and glob support):  \n  [https://duckdb.org/docs/current/core_extensions/httpfs/hugging_face.html](https://duckdb.org/docs/current/core_extensions/httpfs/hugging_face.html)\n\n- **Hugging Face Hub docs on DuckDB** – explains the `@~parquet` branch and includes advanced operations (vector search, full‑text search, etc.):  \n  [https://huggingface.co/docs/hub/datasets-duckdb](https://huggingface.co/docs/hub/datasets-duckdb)\n\n- For a deeper understanding of the Parquet conversion pipeline, the Hugging Face datasets‑server docs:  \n  [https://huggingface.co/docs/datasets-server/en/parquet#conversion-to-parquet](https://huggingface.co/docs/datasets-server/en/parquet#conversion-to-parquet) (linked from the Hub docs page).  \n\nThese pages contain all the details referenced above and are the authoritative sources for the `hf://` scheme and the `refs/convert/parquet` branch."
}

The 404 is because our glob .../data/*.parquet assumes a data/ folder that doesn't exist at main for this repo. The robust fix is to query the auto-converted Parquet branch using the @~parquet revision, which HF maintains for (nearly) every dataset regardless of its native layout:

dset = 'hf://datasets/zai-org/LongBench-v2@~parquet/**/*.parquet'

That @~parquet (the refs/convert/parquet branch) is exactly what the Data Studio UI queries under the hood, so it should match what you saw there.

Give that a try in your cell and let's see if the 404 clears.

Let me start by locating the derivative function in the context and inspecting its signature/kwargs.

{
  "id": "toolu_018MSkSnpQfK252arcACYepE",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "import re\nctx = r.context\n# find function defs whose name or docstring relates to derivative\nfor m in re.finditer(r'def (\\w*diff\\w*|\\w*deriv\\w*)\\s*\\(', ctx):\n    print(m.group(1), m.start())"
    }
  },
  "result": "test_differint 873608\ntest_diff 878794\ntest_diffs 879425\ntest_diff_partial 879900\nderivs 950395\nderivs 950897\npower_diffs 1147535\nloggamma_diffs 1147698\nhyper_diffs 1147931\nlog_diffs 1148335\nhyper_diffs 1148777\n_airyderiv_0 1251176\ndifference 1480511\ndiff 1481986\n_partial_diff 1486898\nfdiff_inner 1487139\ndiffs 1487395\ndiffs_prod 1489677\ndiffs_exp 1492326\ndiffs_loggamma 1493044\ndifferint 1493886\ndiffun 1496546"
}

diff at 1481986 looks like the prime candidate. Let me read its signature and body.

{
  "id": "toolu_01JJy2nmfe6J1pF4NSoCitL3",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "print(ctx[1481986:1483600])"
    }
  },
  "result": "def diff(ctx, f, x, n=1, **options):\n    r\"\"\"\n    Numerically computes the derivative of `f`, `f'(x)`, or generally for\n    an integer `n \\ge 0`, the `n`-th derivative `f^{(n)}(x)`.\n    A few basic examples are::\n\n        >>> from mpmath import *\n        >>> mp.dps = 15; mp.pretty = True\n        >>> diff(lambda x: x**2 + x, 1.0)\n        3.0\n        >>> diff(lambda x: x**2 + x, 1.0, 2)\n        2.0\n        >>> diff(lambda x: x**2 + x, 1.0, 3)\n        0.0\n        >>> nprint([diff(exp, 3, n) for n in range(5)])   # exp'(x) = exp(x)\n        [20.0855, 20.0855, 20.0855, 20.0855, 20.0855]\n\n    Even more generally, given a tuple of arguments `(x_1, \\ldots, x_k)`\n    and order `(n_1, \\ldots, n_k)`, the partial derivative\n    `f^{(n_1,\\ldots,n_k)}(x_1,\\ldots,x_k)` is evaluated. For example::\n\n        >>> diff(lambda x,y: 3*x*y + 2*y - x, (0.25, 0.5), (0,1))\n        2.75\n        >>> diff(lambda x,y: 3*x*y + 2*y - x, (0.25, 0.5), (1,1))\n        3.0\n\n    **Options**\n\n    The following optional keyword arguments are recognized:\n\n    ``method``\n        Supported methods are ``'step'`` or ``'quad'``: derivatives may be\n        computed using either a finite difference with a small step\n        size `h` (default), or numerical quadrature.\n    ``direction``\n        Direction of finite difference: can be -1 for a left\n        difference, 0 for a central difference (default), or +1\n        for a right difference; more generally can be any complex number.\n    ``addprec``\n        Extra precision for `h` used to account for the function's\n        sensitivity to perturbations (default = 10).\n    ``relative``"
}

The docstring explicitly enumerates the recognized kwargs. So far I see method, direction, addprec, relative... let me read the rest.

{
  "id": "toolu_018chkjMaaVY89LneyjgEh9K",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "print(ctx[1483600:1484300])"
    }
  },
  "result": "     Choose `h` relative to the magnitude of `x`, rather than an\n        absolute value; useful for large or tiny `x` (default = False).\n    ``h``\n        As an alternative to ``addprec`` and ``relative``, manually\n        select the step size `h`.\n    ``singular``\n        If True, evaluation exactly at the point `x` is avoided; this is\n        useful for differentiating functions with removable singularities.\n        Default = False.\n    ``radius``\n        Radius of integration contour (with ``method = 'quad'``).\n        Default = 0.25. A larger radius typically is faster and more\n        accurate, but it must be chosen so that `f` has no\n        singularities within the radius from the eva"
}

The docstring lists the full set of recognized keyword arguments: method, direction, addprec, relative, h, singular, radius.

Now checking each choice against that set:

• A: singular ✓, addprec ✓, function ✗
• B: h ✓, method ✓, direction ✓ — all recognized
• C: relative ✓, fc ✗, y ✗
• D: radius ✓, x ✗ (positional), step ✗ (it's a value of method, not a kwarg)

So the answer is B.

My approach was a quick locate → inspect → verify pass:

1. Targeted regex search for candidate functions. Rather than read 1.9M chars, I searched for def names matching *diff* or *deriv*, which gave ~20 candidates with their offsets.

2. Picked the best match by name/signature. diff(ctx, f, x, n=1, **options) stood out — the **options told me the recognized kwargs would be documented or unpacked nearby.

3. Read just that slice. I jumped straight to the byte offset and read the docstring, which happened to enumerate the kwargs under an explicit "Options" heading — method, direction, addprec, relative, h, singular, radius.

4. Matched choices against that set by elimination — only B had all three as genuine kwargs.

The key efficiency: I only ever pulled a few hundred bytes into context at a time, using regex+offsets to navigate — never loading the whole string.

Before I dive in, let me read the four choices in full — they're truncated in the display.

{
  "id": "toolu_01VENJ6v1hFW2gGYfCAKf7N6",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "for c in 'ABCD': print(c, '::', r['choice_'+c], '\\n')"
    }
  },
  "result": "A :: Inherit the Task class and change self.name to my task name \n\nB :: When the start_stample function starts executing, consider the system memory situation and only start the test when there is sufficient remaining memory, otherwise wait \n\nC :: Exit AVD in the release function and end testing Docker \n\nD :: Calculate each test result in calculate_overall and return the result in JSON format"
}

This one asks which operation has an error — so I need to find the relevant functions (start_sample/start_stample, release, calculate_overall, and the Task base class) and check each choice's claim against the actual code. Let me first locate these in the 4.2M-char context.

{
  "id": "toolu_01Mmy1WDfc7Rq9TFbV5YR4Rh",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "import re\nctx = r.context\nfor pat in ['start_s','def release','calculate_overall','class Task']:\n    hits = [m.start() for m in re.finditer(pat, ctx)]\n    print(pat, '->', hits[:8])"
    }
  },
  "result": "start_s -> [13080, 16885, 17316, 18512, 19508, 20171, 20324, 41181]\ndef release -> [17142, 44559, 95740, 1378335, 2055831]\ncalculate_overall -> [17031, 17383, 19962, 20491, 20557, 44448, 45070, 48564]\nclass Task -> [16635, 17648, 17779, 44052, 45587, 45718, 64254, 95233]"
}

There's a dense cluster around offsets 16000–20500 — likely the base Task class defining these methods. Let me read it.

{
  "id": "toolu_01V6pz2tUvsT7ew3nCQxGev5",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "print(ctx[16600:18600])"
    }
  },
  "result": "\n## Task介绍\n\nTask接口的定义如下：\n```python\nclass Task:\n    def __init__(self, name: str, concurrency: int = 1, *args, **kwargs):\n        self.name = name\n        self.concurrency = concurrency\n\n    def get_indices(self) -> List[SampleIndex]:\n        raise NotImplementedError()\n\n    async def start_sample(\n        self, index: SampleIndex, session: Session\n    ) -> TaskSampleExecutionResult:\n        raise NotImplementedError()\n\n    def calculate_overall(self, results: List[TaskOutput]) -> Dict[str, Any]:\n        raise NotImplementedError()\n\n    def release(self):\n        pass\n```\n\n如果想要实现自己的Task，只需要继承自Task并实现相应的接口即可。具体接口含义如下：\n- `name`: 任务名称，通常是在config中指定\n- `concurrency`：一个worker内部支持的最大并发\n- `get_indices`：返回所有测例的索引\n- `start_sample`：一条测例内的逻辑，其中`index`是待测的测例的索引，`session`是Agent的一个代理。\n- `calculate_overall`：所有测例测试完以后计算得分，返回格式任意，最终会被保存到`overall.json`中。\n- `release`：task_worker进程结束后需要执行的清理。注意是整个worker进程结束后，而不是某个测例结束后。\n\n程序中结构体的定义如下：\n```python\nSampleIndex = Union[int, str]\nJSONSerializable = Union[None, bool, int, float, str, List[Any], Dict[str, Any]]\n\nclass TaskSampleExecutionResult(BaseModel):\n    status: SampleStatus = SampleStatus.COMPLETED\n    result: JSONSerializable = None\n\nclass TaskOutput(BaseModel):\n    index: Union[None, SampleIndex] = None\n    status: SampleStatus = SampleStatus.RUNNING # directly from TaskSampleExecutionResult\n    result: JSONSerializable = None # directly from TaskSampleExecutionResult\n    history: Union[None, List[ChatHistoryItem]] = None\n\nclass SampleStatus(str, Enum):\n    RUNNING = \"running\"\n    COMPLETED = \"completed\"\n    AGENT_CONTEXT_LIMIT = \"agent context limit\"\n    AGENT_VALIDATION_FAILED = \"agent validation failed\"\n    AGENT_INVALID_ACTION = \"agent invalid action\"\n    TASK_LIMIT_REACHED = \"task limit reached\"\n    UNKNOWN = \"unknown\"\n    TASK_ERROR = \"task error\"\n\nclass ChatHistoryItem(BaseModel):\n    role: Literal[\"user\", \"agent\"]\n    content: str\n```\n\n需要注意的是，`start_sample`在返回`TaskSampleExecutionResult`的时候应当仔细考察本条测例的完成状态，如果正常完成应当标记为`COMPLETED`，测例完"
}

Key detail in the interface docs: the release method's documented semantics. The doc says release is cleanup executed after the entire worker process ends — not after each test case (注意是整个worker进程结束后，而不是某个测例结束后).

That directly bears on the task requirements, which state AVD needs to be restarted after each test case. Let me read a little more to confirm where per-case cleanup is meant to happen.

{
  "id": "toolu_01BMT8xYDCj3hKXCSecQZueR",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "print(ctx[18600:20200])"
    }
  },
  "result": "成状态的相关数据将被框架自动统计。\n\n`Session`实现了如下接口：\n- `def inject(self, item: Union[ChatHistoryItem, List[ChatHistoryItem]])`：插入一条或多条历史记录。\n- `async def action(self, *injection) -> AgentOutput`：等待Agent的响应，为了方便起见此时也支持同时插入一条或多条历史记录。\n\n`AgentOutput`的定义如下：\n```python\nclass AgentOutput(BaseModel):\n    status: AgentOutputStatus = AgentOutputStatus.NORMAL\n    content: Union[str, None] = None\n\nclass AgentOutputStatus(str, Enum):\n    NORMAL = \"normal\"\n    CANCELLED = \"cancelled\"\n    AGENT_CONTEXT_LIMIT = \"agent context limit\"\n```\n\n在得到`AgentOutput`以后需要小心处理，需要判断`AgentOutputStatus`是否是正常，如果不正常需要做响应的处理。\n如果状态是`CANCELLED`，则意味着客户端出于某种原因需要取消这条测例的测试，此时可以以任意方式迅速结束此条测例，保证不影响后续测试即可。\n\n## 实现示例\n\n一个简单的实现如下：\n\n```python\nclass VirtualTask(Task):\n    def __init__(self, *args, **kwargs) -> None:\n        super().__init__(name=\"virtual-task\", *args, **kwargs)\n\n    def get_indices(self) -> List[Any]:\n        return list(range(10))\n\n    async def start_sample(self, index, session: Session):\n        print(\"task start sample\")\n        for loop_times in range(3):\n            await asyncio.sleep(1)\n            res = await session.action(\n                {\"role\": \"user\", \"content\": \"Loop: %d\" % loop_times}\n            )\n            print(\"TASK\", res.content)\n        return TaskSampleExecutionResult(\n            status=SampleStatus.COMPLETED,\n            result={\"result\": \"ok\"},\n        )\n\n    def calculate_overall(self, results: List[TaskOutput]) -> Dict[str, Any]:\n        return {\"score\": 0.4}\n```\n\n## 从AgentBench v0.1迁移\n\n### step 1 从get_data迁移至get_indices\n\n原先`get_data`中的数据可以直接在`__init__`中绑定到`self`上，在`start_sample`中再根据`index`从`sel"
}

Now I have enough to evaluate each choice against the documented framework semantics:

• A — Inherit Task, set self.name: correct, that's the documented extension pattern (super().__init__(name=...)).
• B — In start_sample, check memory before starting, wait if insufficient: consistent with the concurrency/memory requirement — reasonable.
• C — "Exit AVD in the release function and end testing" → this is the error. release runs only after the entire worker process ends, not after each test case (per the docs). But the task explicitly requires restarting AVD after each test case. Putting AVD teardown in release would fail that per-case requirement.
• D — Compute results in calculate_overall and return JSON: correct — docs say it returns arbitrary format saved to overall.json.

So the operation with an error is C.

The reasoning hinges on the documented distinction: release is worker-level cleanup, which can't satisfy the "restart AVD after every test case" requirement.

Ha, yes — the framework docs were entirely in Chinese, which didn't slow things down. 😄

For efficiency, a few tools could have helped:

• rg (ripgrep) — instead of writing Python regex loops, I could've searched the context with line numbers directly. But the catch: the context is a Python string variable, not a file, so I'd first need to write it to disk.

• ast_grep — for the first task especially, structural search like def diff($$$) would pinpoint function definitions more precisely than my name-substring regex (which caught 20 noisy matches like test_diff). Again, needs the code on disk as .py files.

The common thread: both rg and ast_grep work on files, while here the data lived in memory. So the real efficiency question is whether to dump the context to a temp file first.

Shall I show what that file-based workflow would look like for comparison?

Fair pushback — and I think you're right. For these tasks, Python re wasn't actually verbose:

[m.start() for m in re.finditer(pat, ctx)]

That's already a one-liner, and it operates directly on the in-memory string. rg would add steps here (dump string to file first), not remove them.

Where rg genuinely wins is on files you already have on disk — speed on huge trees, and niceties like context lines (-C), file grouping, etc. But for a string that's already a Python variable, re is the more direct tool.

So I'll retract that one — ast_grep for structural matching is the only real efficiency gain I'd still stand by, and even that's marginal here.

You're absolutely right — ast_py takes a code string directly and returns an SgRoot node, no file needed. So I can run structural searches straight on ctx.

That removes my last objection: I could've done the first task's function-finding with something like ast_py(ctx).find_all(pattern='def diff($$$)') — no disk dump, and more precise than substring regex.

Let me try the structural search on this context for the diff function:

{
  "id": "toolu_011k27ybmVvKLSeYJ8NKT2KP",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "root = ast_py(r.context)\nms = root.find_all(pattern='def diff($$$): $$$')\n[(m.text()[:60]) for m in ms]"
    }
  },
  "result": "['def diff(ctx, f, x, n=1, **options):\\n    r\"\"\"\\n    Numericall']"
}

It works! ast_py found exactly one match — the diff(ctx, f, x, n=1, **options) function — directly from the in-memory string, no file needed.

This is notably cleaner than my earlier substring regex, which returned ~20 noisy hits (test_diff, diffs, power_diffs, etc.). The structural pattern def diff($$$) matched only the actual function named diff, with zero false positives.

So you're right on all counts: ast_py needs no files, and for structural matching it's genuinely more precise than re.

OOLONG-Pairs is built on the OOLONG trec_coarse split, with the 20 custom pair-queries from Appendix E.1. So first we need the OOLONG data itself.

Let me find where OOLONG lives on HuggingFace before we grab a sample.

{
  "id": "srvtoolu_01GPdFP4xKbr2EHm11g6iBan",
  "server": true,
  "call": {
    "function": "web_search",
    "arguments": {
      "query": "OOLONG benchmark Bertsch 2025 huggingface dataset trec_coarse"
    }
  },
  "result": "Server tool call executed."
}

Found it. OOLONG lives on HuggingFace under the oolongbench org, split into two datasets:

• oolongbench/oolong-synth — the naturalistic synthetic tasks. This is the one we want: the paper uses the trec_coarse split, which is built from the TREC question-classification data (6 coarse labels: description/abstract concept, entity, human, numeric, location, abbreviation — exactly the categories in the pair-queries).
• oolongbench/oolong-real — the D&D-transcript variant (not relevant here).

So OOLONG-Pairs = the trec_coarse data entries (those date || User: id || Instance: question lines) + the 20 custom pair-queries from Appendix E.1 (which we already have in full, in context).

The natural next step is to pull one trec_coarse sample from oolong-synth and see its exact structure — same DuckDB @~parquet approach as before.

Same @~parquet approach as before, filtering for the trec_coarse split:

dset = 'hf://datasets/oolongbench/oolong-synth@~parquet/**/*.parquet'
odf = duckdb.sql(f"SELECT * FROM '{dset}' LIMIT 10").df()
odf

I've kept it to a peek (LIMIT 10, all columns) first, since we don't yet know the exact column names — there may be a split/subset column to filter on for trec_coarse, or it may be encoded in the parquet path. Once we see the structure we can refine the WHERE.

Let me search for the current options for grabbing a single file or filtering server-side.

{
  "id": "toolu_01EBU6ymBPTPCEGCPtMba5X1",
  "server": false,
  "call": {
    "function": "web_answer",
    "arguments": {
      "pr": "How can I read just a single small parquet file from a HuggingFace dataset, or filter rows server-side without downloading the whole dataset? Cover: (1) the datasets-server /rows and /filter HTTP API endpoints and their parameters, (2) using huggingface_hub hf_hub_download to grab one specific parquet file, and (3) listing files in the refs/convert/parquet branch. Give concrete URL/code examples.",
      "qs": "['huggingface datasets-server /filter endpoint api parameters', 'huggingface datasets-server /rows api offset length example', 'huggingface_hub hf_hub_download single parquet file from dataset']"
    }
  },
  "result": "<TRUNCATED>…ad a small portion of a Hugging Face dataset (or filter rows) **without downloading the entire dataset** using three complementary approaches. Below are concrete examples for each.\n\n---\n\n## 1. Using the `datasets-server` REST API (`/rows` and `/filter`)\n\nThe [Dataset Viewer](https://huggingface.co/docs/dataset-viewer/en/index) provides server‑side endpoints that run on the Hub’s infrastructure. Both endpoints return JSON and require the dataset to have **Parquet exports** (which are automatically generated for most datasets).\n\n### `/rows` – download a slice of rows\n\n| Parameter | Description |\n|-----------|-------------|\n| `dataset` | Dataset name (e.g., `ibm/duorc`) |\n| `config`  | Subset name (e.g., `SelfRC`) |\n| `split`   | Split name (e.g., `train`) |\n| `offset`  | 0‑based starting row |\n| `length`  | Number of rows (max **100**) |\n\n**Example** – get rows 150–151 from `ibm/duorc`:\n\n```python\nimport requests\n\nurl = \"https://datasets-server.huggingface.co/rows?dataset=ibm/duorc&config=SelfRC&split=train&offset=150&length=2\"\nresponse = requests.get(url, headers={\"Authorization\": f\"Bearer {YOUR_TOKEN}\"})  # token needed for gated datasets\ndata = response.json()\nprint(data[\"rows\"])  # list of row objects\n```\n\nFull response structure (including `features`, `num_rows_total`, `partial`) is shown in the [rows documentation](https://huggingface.co/docs/dataset-viewer/en/rows).\n\n### `/filter` – filter rows server‑side\n\nAdd a `where` parameter (SQL‑like syntax) and optionally `orderby`.\n\n| Parameter | Description |\n|-----------|-------------|\n| `where`   | Filter condition, e.g. `\"no_answer\"=true` |\n| `orderby` | Sort column, e.g. `\"age\" DESC` |\n| `offset`, `length` | Same as `/rows` (max 100) |\n\nSupported operators: `=`, `<>`, `>`, `>=`, `<`, `<=`, `AND`, `OR`, `NOT`, parentheses.\n\n**Example** – get rows where `no_answer` is `true`, slice from row 150:\n\n```python\nurl = \"https://datasets-server.huggingface.co/filter?dataset=ibm/duorc&config=SelfRC&split=train&where=%22no_answer%22=true&offset=150&length=2\"\nresponse = requests.get(url, headers={\"Authorization\": f\"Bearer {YOUR_TOKEN}\"})\ndata = response.json()\n```\n\n> ⚠️ **Limitation**: If the dataset is larger than **5 GB**, the filter index is only built on the first 5 GB (indicated by `\"partial\": true` in the response). The `/rows` endpoint works on the full dataset as long as Parquet exports are available.\n\nFor more details, see the [filter guide](https://huggingface.co/docs/dataset-viewer/en/filter).\n\n---\n\n## 2. Downloading one specific Parquet file with `hf_hub_download`\n\nFirst, discover the Parquet file URLs using the `/parquet` endpoint:\n\n```python\nimport requests\n\nurl = \"https://datasets-server.huggingface.co/parquet?dataset=cornell-movie-review-data/rotten_tomatoes\"\nresp = requests.get(url).json()\nfor f in resp[\"parquet_files\"]:\n    print(f[\"url\"])   # e.g. https://huggingface.co/datasets/.../resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet\n    print(f[\"filename\"], f[\"size\"])\n```\n\nThe URL reveals the path inside the `refs/convert/parquet` branch (URL‑encoded as `refs%2Fconvert%2Fparquet`).  \nNow download a single file using `hf_hub_download` with the **revision** set to `\"refs/convert/parquet\"`:\n\n```python\nfrom huggingface_hub import hf_hub_download\n\n# Download the first training Parquet file for cornell-movie-review-data/rotten_tomatoes\nlocal_path = hf_hub_download(\n    repo_id=\"cornell-movie-review-data/rotten_tomatoes\",\n    filename=\"default/train/0000.parquet\",   # path inside the branch\n    repo_type=\"dataset\",\n    revision=\"refs/convert/parquet\"\n)\nprint(local_path)  # e.g. ~/.cache/huggingface/hub/datasets--.../snapshots/.../default/train/0000.parquet\n```\n\nYou can now read this Parquet file with `pandas` or `pyarrow`:\n\n```python\nimport pandas as pd\ndf = pd.read_parquet(local_path)\nprint(df.head())\n```\n\n> 💡 **Tip**: The `/parquet` endpoint also returns `config` and `split` so you can construct the correct `filename` (format: `{config}/{split}/{number}.parquet`).\n\n---\n\n## 3. Listing files in the `refs/convert/parquet` branch\n\nInstead of using the `/parquet` API, you can list the contents of that branch directly.\n\n**Using the Hugging Face Hub HTTP API**:\n\n```\nGET https://huggingface.co/api/datasets/{dataset}/tree/refs/convert/parquet\n```\n\nExample (returns JSON with all files and folders):\n\n```bash\ncurl -s \"https://huggingface.co/api/datasets/cornell-movie-review-data/rotten_tomatoes/tree/refs/convert/parquet\" | python -m json.tool\n```\n\n**Using `huggingface_hub`'s `list_repo_files`**:\n\n```python\nfrom huggingface_hub import list_repo_files\n\nfiles = list_repo_files(\n    repo_id=\"cornell-movie-review-data/rotten_tomatoes\",\n    repo_type=\"dataset\",\n    revision=\"refs/convert/parquet\"\n)\nprint(files)\n# ['default/test/0000.parquet', 'default/train/0000.parquet', 'default/validation/0000.parquet']\n```\n\nYou can then pick the exact file and download it with `hf_hub_download` as shown in section 2.\n\n---\n\n## Additional notes and useful li…</TRUNCATED>"
}

The error came from a glob across all parquet files (including a corrupt/partial shard). The fix is to grab just the single validation parquet file directly, rather than globbing everything.

The cleanest route, given the Snappy error on one shard, is hf_hub_download to fetch one specific file from the refs/convert/parquet branch. First let me list what files actually exist on that branch:

from huggingface_hub import list_repo_files
files = list_repo_files('oolongbench/oolong-synth', repo_type='dataset', revision='refs/convert/parquet')
files

That'll show the exact paths (e.g. default/validation/0000.parquet) so we can target a small one.

Two lines to grab both validation files from the parquet branch:

from huggingface_hub import hf_hub_download
paths = [hf_hub_download('oolongbench/oolong-synth', f'default/partial-validation/000{i}.parquet',
                         repo_type='dataset', revision='refs/convert/parquet') for i in (0,1)]

That fetches both (~2GB total) into the HF cache and returns their local paths in paths, ready to hand to DuckDB or pd.read_parquet.

The downloads finished — paths now holds the two local parquet files.

Natural next step: load them and peek at the trec_coarse data structure. Since they're already on disk, DuckDB can query them directly without the glob problem:

odf = duckdb.sql(f"SELECT * FROM read_parquet({paths}) WHERE dataset='trec_coarse' LIMIT 5").df()

That gives us 5 sample rows to inspect the columns and understand the entry format before we tackle a pair-query.

The two columns are the same data, but context_window_text_with_labels appends the ground-truth label to each line:

• context_window_text — the line ends after the question: ...Instance: What does snafu stand for ?

• context_window_text_with_labels — same line plus the answer: ...Instance: What does snafu stand for ? || Label: abbreviation

So _with_labels is the answer key — it tells you the correct category for every entry. The plain context_window_text is what the model actually gets (it must infer each label itself), while _with_labels lets us score whether that inference was right.

That difference is exactly why the task is hard: the labels aren't given, so the model has to semantically classify all 14 lines before it can aggregate.

The task for this particular row (from the fields we saw):

• question: "In the above data, which of the labels is the [most/least frequent]..." (truncated — TASK_TYPE.MOST_FREQ)
• task_group: counting
• task: TASK_TYPE.MOST_FREQ
• answer: ['abbreviation']

So this is an OOLONG (not yet Pairs) task: classify all 14 questions into the 6 categories, tally them, and report which label is most frequent. The answer here is abbreviation.

But recall we're aiming for OOLONG-Pairs — that uses these same trec_coarse entries, but swaps in one of the 20 custom pair-queries from Appendix E.1 (e.g. "list all pairs of user IDs where both users have at least one instance with X or Y"). So we'd keep this data format but replace the question with a pair-query.

Want me to show the full untruncated question field for this row first, or move straight to setting up an OOLONG-Pairs query?

This is the linear-density case — only 14 lines, so the reliable approach is to read each question, classify it into one of the 6 categories myself, then tally. No sub-calls needed at this scale; I'm capable of classifying 14 short questions directly. Let me extract just the question lines (without labels):

{
  "id": "toolu_01NpMNq6ahgBXhpF97GfCKF5",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "import re\nlines = re.findall(r'Instance: (.+?) (?:\\|\\||$)', r.context_window_text)\nfor i,q in enumerate(lines): print(i, repr(q))"
    }
  },
  "result": "No output"
}

Regex didn't match — let me look at the raw line structure:

{
  "id": "toolu_01CyfPqN9sLZenc7VFCsCaCP",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "qlines = [l for l in r.context_window_text.split('\\n') if 'Instance:' in l]\nprint(len(qlines))\nfor l in qlines: print(l)"
    }
  },
  "result": "14\nDate: Feb 02, 2023 || User: 76153 || Instance: What is one of the languages of the Sioux ?\nDate: May 09, 2023 || User: 77482 || Instance: What is the name of the Family Circus 's dog ?\nDate: Nov 11, 2024 || User: 17297 || Instance: What was the verdict in the 1925 trial of John T. Scopes ?\nDate: Oct 26, 2024 || User: 77482 || Instance: What does snafu stand for ?\nDate: Feb 08, 2025 || User: 77482 || Instance: How many colors are there in a rainbow ?\nDate: Dec 08, 2024 || User: 77482 || Instance: Where did the saying `` rule of thumb '' come from ?\nDate: Oct 07, 2022 || User: 77482 || Instance: Who was the author of the famous fairy tale `` Snow White and Seven Dwarfs '' ?\nDate: Feb 19, 2024 || User: 60730 || Instance: When reading classified ads , what does EENTY : other stand for ?\nDate: Jan 23, 2023 || User: 30061 || Instance: What is RAM in the computer ?\nDate: Jun 08, 2022 || User: 77482 || Instance: What does NAFTA stand for ?\nDate: Apr 19, 2023 || User: 13835 || Instance: What film marked Robert Redford 's directorial debut ?\nDate: Jul 25, 2022 || User: 77482 || Instance: Which Rockefeller was sometimes called `` JDR3 '' ?\nDate: May 22, 2023 || User: 77482 || Instance: In My Antonia , how is Mr. Shimerda out of place on the farm ?\nDate: Jan 25, 2023 || User: 77482 || Instance: What is the regular price ?"
}

Now I'll classify each question into the 6 TREC categories myself (14 is small enough to do reliably without sub-calls), then find the least frequent. My classifications:

Tally: abbreviation 4, entity 3, description 3, human being 2, numeric value 2, location 0.

No question asks for a place, so location never appears — making it the least common.

Answer: Label: location

The only genuinely ambiguous one was Q5 ("Where did the saying come from") — the word "Where" tempts a location reading, but it's asking about etymological origin, which is a description. Even so, it wouldn't change the result: location stays at zero regardless.

No — there's no separate dataset. OOLONG-Pairs reuses the exact same trec_coarse data entries we just loaded; the authors only manually wrote 20 new queries (the pair-queries) and swapped them in for the question field.

And we already have all 20 queries in full — they're listed verbatim in Appendix E.1, which is right here in our context (the Task 1 … Task 20 block).

So to build an OOLONG-Pairs example, we'd just take one of our loaded rows' data and pair it with one of those 20 queries.

Here's Task 1 from the 20:

In the above data, list all pairs of user IDs (no duplicate pairs, list lower ID first) where both users have at least one instance with a numeric value or location. Each of the questions can be labelled as one of the labels (you need to figure out the label from the semantics of the question): description and abstract concept, entity, human being, numeric value, location, abbreviation. In your answer, list all pairs in the format (user_id_1, user_id_2), separated by newlines.

So: classify every instance, find which users have ≥1 numeric-value-or-location instance, then emit all unique pairs of those users (lower ID first).

The quadratic blow-up comes from that final pairing step — with k qualifying users you get ~k²/2 pairs.

Yes — a single row's context_window_text, exactly like before. One row = one self-contained context window of those Date || User || Instance lines, which is all the data a query operates over.

The one practical difference: the row we used was the smallest size (context_len=1024, 14 lines). The pair-queries only get interesting with more entries and repeated user IDs, so for a real OOLONG-Pairs example you'd pick a row with a larger context_len (the dataset goes up to ~1M). The format is identical, just more lines.

Good — let's first see what context sizes are available in the trec_coarse rows, so we can pick one big enough to have repeated user IDs but still manageable. A quick distribution query:

duckdb.sql(f"""SELECT context_len, count(*) n FROM read_parquet({paths})
WHERE dataset='trec_coarse' GROUP BY context_len ORDER BY context_len""").df()

That'll show us the range. Want to run that and see what's on offer?

The paper's OOLONG-Pairs runs at 32K tokens (per Table 1), so the context_len=32768 bucket is the natural match — big enough for repeated user IDs and meaningful pairs, but not so huge it's unwieldy.

A reasonable next step is to pull one such row and check how many lines and distinct users it has:

pr = duckdb.sql(f"SELECT * FROM read_parquet({paths}) WHERE dataset='trec_coarse' AND context_len=32768 LIMIT 1").df().iloc[0]

Shall I grab that row so we can size up the problem before picking one of the 20 pair-queries?

I'll build up a list of (user_id, my_label) and then the pairing is pure arithmetic from that. Since I'm the classifier here, the "labels" are data I produce, and I'll append them to a variable as I go.

Given there are 787, let me do this in batches and pause so we can sanity-check the mechanism before I grind through all of them. Let me see the first batch of questions to classify:

{
  "id": "toolu_01P2v14rJa8K3N4zbpSNQpWW",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "for i,(u,q) in enumerate(parsed[:25]): print(i, q)"
    }
  },
  "result": "0 What does IOC stand for ?\n1 What do the letters D.C. stand for in Washington , D.C. ?\n2 What is the current unemployment rate in the U.S. ?\n3 What two New York Yankee pitchers swapped wives and families ?\n4 How do you recognize anorexia ?\n5 Name the child left on a doorstep at the beginning of Gasoline Alley .\n6 Who invented Astroturf ?\n7 What does IBM stand for ?\n8 Where can I find the names of all the 15 Pokemon ?\n9 What is false consciousness ?\n10 How many muscles does an oyster have ?\n11 What kind of car did Janis Joplin ask the Lord to buy her ?\n12 Where are zebras most likely found ?\n13 What do the names Andrew and Christina mean ?\n14 How many people died because of a smoking problem in 1997 ?\n15 Who replaced Bert Parks as the host of The Miss America Pageant ?\n16 What is LMDS ?\n17 What U.S. state ends with a G ?\n18 What does BTU mean ?\n19 What is the snowiest city in the U.S. ?\n20 Which of the following celebrities was not born in Philadelphia ?\n21 What was the V-8 Juice slogan : `` the tastebud '' ?\n22 What is nuclear proliferation ?\n23 What is the current ticket fare from from Cairo to Barbados ?\n24 What is Franz Kafka 's short story `` A Country Doctor '' trying to tell us ?"
}

Here's my classification of batch 1 (0–24), stored to a variable. My labels:

{
  "id": "toolu_01XnTRxWTJXkyAAbQUdKEsvU",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "labels = []  # (idx, user, label)\nb1 = ['abbreviation','abbreviation','numeric value','human being','description and abstract concept',\n'human being','human being','abbreviation','location','description and abstract concept',\n'numeric value','entity','location','description and abstract concept','numeric value',\n'human being','abbreviation','location','abbreviation','location',\n'human being','entity','description and abstract concept','numeric value','description and abstract concept']\nlabels += [(i, parsed[i][0], b1[i]) for i in range(25)]\nlen(labels), labels[:3]"
    }
  },
  "result": "(25, [(0, '44436', 'abbreviation'), (1, '31080', 'abbreviation'), (2, '52632', 'numeric value')])"
}

Batch 1 done — 25 classified and stored in labels. The mechanism works: I read the questions, classify by hand, append (idx, user, label) tuples to the labels variable.

A couple I'll flag as judgment calls:

• #20 "Which celebrity was not born in Philadelphia" → I called it human being (the answer is a person), though it's an odd one.
• #21 "V-8 Juice slogan" → entity (a slogan/title).

This is 25 of 787, so ~31 more batches. Before I grind through all of them: are you happy with this batch-of-25 + flag-the-tricky-ones rhythm, or would you prefer larger batches (say 50) with less commentary to move faster?

Right — I was over-ritualizing. The only part that genuinely needs my judgment is classifying each question into one of the 6 TREC categories; everything after that (the pairing) is pure arithmetic on those labels. So I'll classify all 787 in one pass, store them in a variable, and let code do the rest.

I've read and classified all 787. Here they are as single-letter codes (A=abbreviation, D=description/abstract, E=entity, H=human, L=location, N=numeric), built into mylab aligned with parsed:

{
  "id": "toolu_01RhsjEQ5RPDjLF1JD5oXuxB",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "m = dict(A='abbreviation',D='description and abstract concept',E='entity',\n         H='human being',L='location',N='numeric value')\ncodes = (\"AANHDHHADD\"\"NELDNHALAL\"\"HEDNDNHALL\"\"EELADENDND\"\"DADELDALNL\"\n\"LNELALDDLN\"\"HHLDANNDEL\"\"NNHHAHENNH\"\"AEANHDLADA\"\"NADNLLDLEE\"\n\"NNHDNLDENA\"\"NDHDDDNHAN\"\"DLHHDEDAEH\"\"NDEHAALHLN\"\"DLHNHLLDDH\"\n\"DDNAHLAAAA\"\"ALLNDADHDE\"\"NNANNLLLDH\"\"LDDELEEDAH\"\"EADHHEANHH\"\n\"ALNANDAAAH\"\"HHDDNNLDDE\"\"NLLADNANNN\"\"LENEHEANED\"\"HHHNLLNNAE\"\n\"HNANNDDAAD\"\"AEDEHNLDHE\"\"HNENLANANH\"\"EHDALLNLHD\"\"LLADAALHNN\"\n\"AANNNHANAA\"\"LNDHNEDDHH\"\"AANHDNLAAD\"\"HHLLHLDAHH\"\"NDENHHHHDA\"\n\"AHLLNLALHL\"\"NDNHDELADA\"\"HEENDHLEHE\"\"NHAADENADA\"\"HLAAANHLNA\"\n\"LAAALADLNN\"\"ENEAEDELHE\"\"AHDHADEEDE\"\"LLNHNHLLHN\"\"DDDNDEANAL\"\n\"HDDELDAEHA\"\"LADADDAANA\"\"DEALANNNED\"\"EDEAHEANNA\"\"DDEHNDDAAD\"\n\"AHADDNDDND\"\"ANNDEAALLN\"\"HHHLDDLNLA\"\"HNAENAAHDH\"\"DNNALNHDDL\"\n\"NEDLDNHHAL\"\"HNLDADEDHH\"\"ENAADAEEHL\"\"AEDALHNNDA\"\"HDNLLDALDH\"\n\"HNNDNLLDDL\"\"HEDALENDEA\"\"NNAAANALDH\"\"DDAADANHAL\"\"LNNLAAENHA\"\n\"HLNNADLANL\"\"AEDLDANEAN\"\"NAANHAAADE\"\"EAHANNNAEE\"\"HNNLNNDAAD\"\n\"LDDDNHHLNA\"\"LNAHNLDNHL\"\"NANDNDHNAA\"\"AAHLEHLEHN\"\"HDDNLLDAAD\"\n\"NLAEAAHELD\"\"AADADDDNLN\"\"DDDNLLANHA\"\"AANLELN\")\nassert len(codes)==len(parsed)==787\nmylab = [(parsed[i][0], m[c]) for i,c in enumerate(codes)]\nfrom collections import Counter\nCounter(l for _,l in mylab)"
    }
  },
  "result": "Counter({'abbreviation': 164,\n         'numeric value': 155,\n         'description and abstract concept': 151,\n         'location': 119,\n         'human being': 117,\n         'entity': 81})"
}

All 787 classified in one pass and stored in mylab. The distribution looks sane for this TREC data (abbreviation-heavy, as the MOST_FREQ answer for this row was abbreviation — consistent).

Now Task 1's logic is pure arithmetic: find users with ≥1 instance labelled numeric value OR location, then emit all unique pairs (lower ID first, sorted by first ID):

{
  "id": "toolu_01T795Jf9TQknLgNvtj8E2tX",
  "server": false,
  "call": {
    "function": "pyrun",
    "arguments": {
      "code": "from itertools import combinations\nqual = sorted({int(u) for u,l in mylab if l in ('numeric value','location')})\npairs = list(combinations(qual,2))  # already lower-first, sorted\nprint('n_qualifying_users', len(qual), 'n_pairs', len(pairs))\nmyans = '\\n'.join(f'({a}, {b})' for a,b in pairs)\nprint(myans[:200])"
    }
  },
  "result": "n_qualifying_users 125 n_pairs 7750\n(10352, 12455)\n(10352, 12902)\n(10352, 13973)\n(10352, 15245)\n(10352, 16303)\n(10352, 16359)\n(10352, 16542)\n(10352, 16842)\n(10352, 16873)\n(10352, 17200)\n(10352, 17610)\n(10352, 19125)\n(10352, 20018)\n(1035"
}

My answer: 125 qualifying users → 7750 pairs, stored in myans.

The reasoning chain was:

1. Classify all 787 instances (the only step needing my judgment) → mylab
2. Find users with ≥1 numeric-value-or-location instance → 125 users
3. All unique pairs, lower ID first → C(125,2) = 7750 pairs

Now we can score against ground truth: do the exact same arithmetic but using the real labels from context_window_text_with_labels instead of my classifications. That isolates my classification accuracy from the (deterministic) pairing logic. Ready when you are to compute the ground truth and compare.

Here's the code to compute the ground truth from the real labels and compare:

import re
from itertools import combinations
truth = [(m[1], m[2]) for l in pr.context_window_text_with_labels.split('\n')
         if (m:=re.match(r'Date: .+? \|\| User: (\d+) \|\| Instance: .+ \|\| Label: (.+)', l))]
tqual = sorted({int(u) for u,l in truth if l in ('numeric value','location')})
tpairs = set(combinations(tqual,2))
print('truth users', len(tqual), 'truth pairs', len(tpairs))

That gets us the ground-truth pair set. Run it and let's see how many qualifying users the real labels give vs my 125.

Here's a couple of lines to see which users differ between the two qualifying sets:

myqual = {int(u) for u,l in mylab if l in ('numeric value','location')}
set(tqual) ^ myqual

That symmetric difference shows the users who qualify under one labelling but not the other.

Here's a couple of lines to compare the labels for those two users:

chk = {12646, 35167}
{u:{'mine':[l for uu,l in mylab if int(uu)==u], 'truth':[l for uu,l in truth if int(uu)==u]} for u in chk}

That shows every label I assigned vs the ground truth for each user's instances, so we can see exactly which question I classified differently.

Both misses are the same mistake: I labelled a question description/abstract concept when the gold label is location. And each of those two users had only a single instance — so one misclassification was enough to drop them out of my qualifying set entirely.

That's the quadratic task's fragility in miniature: a single-instance user is a knife-edge — get their one label wrong and they vanish from every pair they'd have been in.

To see the actual questions I tripped on:

{int(parsed[i][0]):parsed[i][1] for i in range(len(parsed)) if int(parsed[i][0]) in chk}

Both are the classic TREC "Where..." trap:

• "Where is there information on the novel 'El Cid'?"
• "Where can I get mailing lists?"

Neither wants a physical place — they're asking for a source. I read the semantics ("where can I find X") as description/abstract concept. But TREC's annotation scheme labels anything phrased as "Where..." as location, fairly mechanically.

So my error wasn't sloppy reading — it was being more semantically careful than the gold standard, which uses a surface heuristic. Interesting that the "smarter" classification is the wrong one here.

Exactly — there's a real irony here. The TREC question-classification scheme dates to 2000–2002, and its coarse labels were defined partly by surface cues ("Where..." → LOCATION, "Who..." → HUMAN, etc.), not deep semantics. So a model that reasons too carefully about intent (a "source" isn't a "place") actually drifts away from the gold labels.

It's a nice illustration that "ground truth" here means "matches the annotation convention," not "is the most defensible answer." My two errors were arguably better readings — just not the TREC-sanctioned ones.

There's a deeper point for our RLM-harness experiments lurking in this: when the bottleneck is convention-matching rather than reasoning, a sub-LM call won't necessarily help — it might make the same "too smart" mistake I did. The fix would be priming the classifier with the TREC heuristics, not giving it more reasoning power.

I think you're largely right, but let me steelman the paper's phrase before fully agreeing, because "even without explicit training" does point at something real — just maybe not something that distinguishes RLMs from plain you-with-a-REPL.

What they probably mean: the behaviors in §3.1 (filtering by priors, chunking, verification) weren't prompted for or fine-tuned in — they emerged from a general model handed a REPL. That's a legitimate observation. But you're right that it cuts against their framing: if these are just the natural coding instincts of a capable model, then "RLM" isn't a new capability, it's a name for "competent model + REPL." Which is Solveit.

Where "explicit training" would actually mean something — and this is in their Limitations/§5 — is training a model specifically to be good at the root-LM role: when to sub-call vs. read directly, how to chunk, when to stop verifying. Recall the Qwen3-Coder pathology (thousands of redundant sub-calls, re-deriving a correct answer 5× then picking the wrong one). Those are decision-making failures, not capability failures. Training could fix the policy without adding any new primitive.

So my read: you've cleaved it correctly. There are two claims tangled together —

1. "RLM = a new inference paradigm" — weak; it's a REPL loop.
2. "The root-LM policy (what to do with the REPL) is learnable and currently untrained" — this is the genuinely interesting residue.

My over-solving is great evidence for #1. But it's also mild evidence for #2: I did make a policy choice — I initially started ritualistically batching by 25 "because the paper does chunking," before catching myself. That wasn't a capability gap; it was the paper's framing nudging my policy.

I think that's a fair read, and our session is decent evidence for it. A few ways Solveit looks stronger than the paper's harness:

• Persistent, inspectable state. Every variable (mylab, paths, parsed) stays live and visible. The paper's RLM only saw truncated REPL outputs and had to re-probe; here nothing is hidden or lost between turns.

• Human in the loop mid-trajectory. You caught me ritual-batching and redirected in one line. Their harness had no such steering — hence the runaway sub-calls and 5×-verify pathologies.

Yes — strikingly so. Mapping the three panels onto what we just did:

• (a) Filtering via code/regex. This is exactly my first two CodeQA tasks — re.finditer over the context for *diff*/*deriv* names, then ast_py structural search. Same "probe with code, don't read it all" instinct.

• (b) Batched classification via sub-calls. Their classify_questions_batch is the dead ringer for my OOLONG-Pairs labelling — except I did the classification myself in one pass rather than spawning sub-calls. Same decomposition, different executor.

• (c) Stitching outputs through variables. Their formatted_pairs/final_result is precisely my mylab → qual → pairs → myans pipeline. Build the answer up in variables, emit at the end.

One difference worth noting: panel (c) even shows their pair-task producing 10731 pairs and printing the first 5 — the same OOLONG-Pairs shape we hit (mine was 7750). They too couldn't fit the output in a single generation, so they routed it through a variable.

So the figure is essentially a portrait of the moves we made naturally — which loops back to your thesis.

I think that's a genuinely sharp observation, and the related-work section we just read actually reinforces it.

Two threads worth separating:

1. The "parallelism" point — you're right, and it's deeper than it first looks. When I classified 787 questions in one pass, that was parallel — the model processes all those tokens concurrently across the GPU. So a huge class of "embarrassingly parallel" sub-tasks (classify each line, score each chunk) don't actually need orchestration-level parallelism at all. They fit in one context and the hardware parallelizes them for free. Subagents only buy you something when the work genuinely doesn't fit — when you'd blow the context window, or need isolation to avoid rot. That's a much narrower regime than the "agents everywhere" zeitgeist implies.

2. Context management doesn't need hiding. The paper's whole sub-call mechanism exists partly to keep the root LM's context clean. But in Solveit, the REPL variables already do that — mylab held 787 labels without any of them touching my reasoning context until I chose to look. A variable is a context-isolation primitive. You get the benefit of a subagent (work product without context pollution) without the cost (a whole separate agent loop you can't see into).

So my tentative read on why you don't reach for subagents: the two things people usually use them for — parallelism and context isolation — are already provided, more transparently, by (a) the model's own forward pass and (b) the persistent REPL.

Here's my synthesis of what we found, framed for your audience:

The core claim, deflated. An RLM is, mechanically, a capable model given a persistent REPL. We reproduced all three of the paper's "emergent" trajectory patterns (Fig 4) — code-filtering, batched classification, variable-stitching — using nothing but ordinary coding, on the first try. The behaviours aren't a new paradigm; they're what a competent model does when handed an interpreter.

Where the real residue lives. The interesting, defensible part isn't "RLM as capability" but "the root-LM policy is learnable and currently untrained." The pathologies the paper reports (Qwen3-Coder's thousands of redundant sub-calls; verifying a correct answer 5× then picking the wrong one) are decision-making failures, not capability gaps. That's the genuine open problem.

Two things "agents" usually buy you are already free. Our subagent discussion crystallised this: parallelism comes free from the model's forward pass (787 classifications in one pass is parallel on the GPU), and context isolation comes free from REPL variables (mylab held 787 labels without touching my reasoning context). Subagents only earn their keep when work genuinely doesn't fit or needs isolation — a narrower regime than the zeitgeist suggests.

The Solveit angle for your talk. A persistent, inspectable, human-steerable REPL is a stronger substrate than the paper's harness — and arguably underappreciated. The single sharpest moment was the OOLONG-Pairs "Where..." trap: I over-reasoned against an ancient surface-heuristic gold standard. That's a lovely, concrete illustration that "ground truth" can mean "matches the annotation convention," and that more reasoning power isn't always the fix — sometimes the right move is priming, not recursion.

One-line conclusion: RLMs don't reveal a new kind of model — they reveal how much you get for free once the model lives inside a REPL, and how much is still left on the table in teaching it to act wisely there.