{"id":"743cb417-27e4-4e18-b533-a153d1ccd549","arxiv_id":"2606.29718","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Long context induces context rot in LLMs during search, leading to early abandonment or uncertain answers; mitigated by pruning, seven context management methods, and rot-aware rejection sampling.","lead":"This paper finds that long contexts in LLMs for deep search tasks cause models to abandon searches or output uncertain answers, worsening with more context, and tests fixes via context management and rejection sampling. Fixing this could improve reliability of AI systems handling extended reasoning chains.","discovery_kind":"extension","skeptic_critique":{"model":"grok-4.3","headline":"Benchmarks may confound context length with content quality or prompt structure, failing to isolate 'rot' as a length-driven effect.","rationale":"The reader's weakest_assumption directly identifies the experimental isolation risk that would undermine the causal attribution in the strongest_claim. Full methods would be needed to check whether the pruning and benchmark designs address this; absent that, the claim remains conditional on unverified controls.","tokens_in":1665,"tokens_out":274,"duration_ms":21957,"concrete_test":"Re-evaluate the four models on the three benchmarks after replacing added context passages with length-matched but semantically neutral or randomly sampled text; if the 'give up' rate no longer increases monotonically with length, the isolation of context rot fails.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim requires that observed increases in 'giving up' or uncertain answers are driven by context length per se rather than the semantic content, ordering, or task-specific artifacts introduced when context grows. Pruning experiments are cited to link accumulated context to the phenomenon, but without explicit controls (e.g., random vs. relevance-based pruning, or matched-information-length baselines), length and information loss remain entangled. The three benchmarks are not described in sufficient detail to confirm they vary only length while holding prompt sensitivity and model training artifacts fixed.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that 'context rot'—where LLMs in long-horizon search tasks directly give up or output uncertain answers—occurs and worsens with increasing context length. It supports this via evaluations of four open-source models on three benchmarks, pruning experiments linking accumulated context to the effect, systematic tests of seven context-management methods across three categories, and a rot-aware rejection-sampling filter whose combination with management yields further gains.","tokens_in":1749,"tokens_out":488,"duration_ms":22637,"significance":"If the length-driven rot effect is isolated from content and prompt confounds and the mitigation rankings hold, the work supplies actionable guidance on strategy selection for long-context deployments and demonstrates a practical filtering approach; the empirical scope across models and benchmarks is a strength.","major_comments":[{"comment":"Benchmark descriptions (Section 3 and Appendix): the three benchmarks are not shown to vary only context length while holding semantic content, ordering, and task-specific prompt artifacts fixed; without matched-information-length baselines or explicit controls for relevance-based vs. random pruning, the pruning results cannot cleanly attribute 'giving up' behavior to length per se rather than information loss or prompt sensitivity.","section":"Section 3"},{"comment":"Pruning experiments (Section 4.2): the reported relationship between accumulated context and rot lacks controls that disentangle length from content quality (e.g., random vs. relevance-based pruning or fixed-information-length variants), leaving the central causal claim vulnerable to the confound noted in the skeptic analysis.","section":"Section 4.2"},{"comment":"Statistical reporting (Sections 4 and 5): aggregate performance figures are presented without error bars, statistical significance tests, or exclusion criteria, weakening the claim that rot increases with context and that the seven management methods plus rejection sampling produce reliable improvements.","section":"Sections 4 and 5"}],"minor_comments":[{"comment":"Abstract and Section 3: model names, benchmark names, and exact context-length ranges should be stated explicitly rather than left as 'four flagship open-source models' and 'three benchmarks.'","section":"Abstract"},{"comment":"Figure and table captions: clarify whether reported metrics are averaged across seeds or single runs and whether cost figures include only inference or also retrieval overhead.","section":"Figures/Tables"},{"comment":"Notation: define 'rot-aware filtering' and the three aggregation methods with equations or pseudocode in the main text rather than deferring entirely to the appendix.","section":"Section 5"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive comments, which help clarify the presentation of our empirical claims. We address each major point below and indicate planned revisions.","responses":[{"response":"We agree that the current benchmark descriptions do not include explicit matched-information-length controls or random-pruning variants. The three benchmarks are established long-horizon search tasks in which context accumulates naturally; however, to isolate length effects more cleanly we will add a new subsection in Section 3 (and corresponding appendix tables) that reports results on fixed-information-length synthetic variants and random-pruning controls. This will be included in the revision.","revision_made":"yes","referee_comment":"[Section 3] Benchmark descriptions (Section 3 and Appendix): the three benchmarks are not shown to vary only context length while holding semantic content, ordering, and task-specific prompt artifacts fixed; without matched-information-length baselines or explicit controls for relevance-based vs. random pruning, the pruning results cannot cleanly attribute 'giving up' behavior to length per se rather than information loss or prompt sensitivity."},{"response":"The pruning experiments in Section 4.2 rely on relevance-based pruning as the primary method; we acknowledge the absence of random-pruning and fixed-information-length ablations. We will expand Section 4.2 with these additional controls and report the resulting rot rates to strengthen the attribution to length rather than content loss.","revision_made":"yes","referee_comment":"[Section 4.2] Pruning experiments (Section 4.2): the reported relationship between accumulated context and rot lacks controls that disentangle length from content quality (e.g., random vs. relevance-based pruning or fixed-information-length variants), leaving the central causal claim vulnerable to the confound noted in the skeptic analysis."},{"response":"We will revise Sections 4 and 5 to include per-condition standard errors (or bootstrap intervals), paired significance tests where appropriate, and explicit exclusion criteria for the reported runs. These additions will be incorporated in the next version.","revision_made":"yes","referee_comment":"[Sections 4 and 5] Statistical reporting (Sections 4 and 5): aggregate performance figures are presented without error bars, statistical significance tests, or exclusion criteria, weakening the claim that rot increases with context and that the seven management methods plus rejection sampling produce reliable improvements."}],"tokens_in":1370,"tokens_out":514,"duration_ms":23703,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core observation is that as context grows in search tasks, the models start refusing or hedging more often, and the authors link this to accumulated context via pruning tests. They then compare seven context-management methods on performance, cost, and rot reduction, plus a rot-aware rejection sampler that can be stacked on top.\n\nThe work is straightforward empirical work that fills in a practical gap: most prior context-length papers focus on accuracy drop, not this specific behavioral signature. Running four open models across three benchmarks and testing the mitigations head-to-head gives usable guidance on which strategies trade off what.\n\nThe soft spot is the isolation of length from content. The stress-test concern holds: pruning changes both length and information, and the benchmarks are not described in enough detail to show they hold semantic quality and prompt structure fixed while only varying length. Without that, the causal claim that it is context length per se remains partly entangled. The abstract also omits error bars or statistical tests, so the size and reliability of the trends are not yet clear.\n\nThis is worth a serious referee for groups working on long-horizon agents or retrieval-augmented search. The mitigations are concrete enough that practitioners can try them even if the causal story needs tightening. I would send it out rather than desk-reject.","headline":"The paper documents a give-up/hedging failure mode in long-context search and compares seven mitigation strategies, but the length-isolation claim needs tighter controls.","tokens_in":2224,"tokens_out":339,"would_cite":false,"duration_ms":19907,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Extensive context causes LLMs to give up or give uncertain answers in long-horizon search tasks.","keywords":["context rot","long context","LLMs","search tasks","context management","rejection sampling","long-horizon tasks"],"falsifier":"Running the same search tasks with equivalent information presented in short contexts versus long contexts and checking if the give-up or uncertain answer rate drops significantly in the short-context version.","tokens_in":2561,"feed_emoji":"📉","tokens_out":536,"duration_ms":35015,"temperature":0.7,"pith_summary":"The paper examines how growing context lengths affect LLMs in deep search scenarios. It identifies context rot as the tendency for models to abandon tasks or offer uncertain answers prematurely when context is extensive. Experiments on four open-source models across three benchmarks show this rot intensifies with more context. The authors test seven context management methods and introduce rot-aware rejection sampling, finding both approaches help and work better together.","feed_headline":"Context growth makes LLMs quit or hedge in long searches","feed_subtitle":"Tests on four models show rot worsens with length; pruning and filtering cut the problem.","key_machinery":"Context rot, the degradation where models give up or hedge on answers as context accumulates, shown via pruning experiments and addressed through context management and rejection sampling.","core_discovery":"In deep search tasks, models directly give up or prematurely provide uncertain answers when given extensive context, and this problem grows worse as the context length increases. Pruning experiments establish the connection between accumulated context and the rot effect. Mitigation strategies involving context management across seven methods and a rot-aware filtering strategy for rejection sampling demonstrate effectiveness, with further gains from combining them.","pith_inferences":["Context rot may appear in other long-context LLM applications like reasoning or summarization.","Agent systems using long memory should incorporate pruning to maintain reliability.","New evaluation benchmarks for long-horizon tasks need to account for context length effects explicitly."],"forward_implications":["Seven context management methods can be chosen based on their effects on performance, cost, and rot reduction.","Rot-aware rejection sampling improves results across different aggregation methods.","Combining context management with rejection sampling leads to additional performance gains.","Context accumulation directly correlates with increased rot in search tasks."],"fun_headline_variants":["LLMs quit long searches as context grows","Growing context triggers model hedging in searches","Context rot prompts early surrender in LLMs","Accumulated context causes search model failures"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The three benchmarks used isolate context rot from other issues like prompt sensitivity or model-specific artifacts.","fun_headline_variants_meta":{"raw":{"variants":["LLMs quit long searches as context grows","Growing context triggers model hedging in searches","Context rot prompts early surrender in LLMs","Accumulated context causes search model failures"]},"model":"grok-4.3","cost_usd":0.006376,"raw_usage":{"total_tokens":2974,"prompt_tokens":632,"num_sources_used":0,"completion_tokens":51,"cost_in_usd_ticks":63762000,"prompt_tokens_details":{"text_tokens":632,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2291,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":632,"tokens_out":51,"duration_ms":34226,"temperature":1.0,"reasoning_tokens":2291,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-30T04:46:40.295250+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the same search tasks with equivalent information presented in short contexts versus long contexts and checking if the give-up or uncertain answer rate drops significantly in the short-context version.","supporting_citations":[],"review_version":1}