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ARXIV:2604.14140 · LLM EVALUATION · SUBMITTED 16 APR · 18:18 UTC · FRESHNESS STALE
ARXIV:2604.14140LLM EVALUATIONSUBMITTED 16 APR · 18:18 UTCFRESHNESS STALESumeet Ramesh Motwani · Daniel Nichols · Charles London · Peggy Li · Fabio Pizzati · Acer Blake · +14 at arXiv
Introducing LongCoT, a benchmark for evaluating long-horizon chain-of-thought reasoning in LLMs, revealing a significant gap in current model capabilities and providing a rigorous measure for future progress.
Opportunity summary
Pain Introducing LongCoT, a benchmark for evaluating long-horizon chain-of-thought reasoning in LLMs, revealing a significant gap in current model capabilities and providing a rigorous measure for future progress.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Introducing LongCoT, a benchmark for evaluating long-horizon chain-of-thought reasoning in LLMs, revealing a significant gap in current model capabilities and providing a rigorous measure for future progress. An essential component of this ability is…
As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this ability is planning and managing a long, complex chain-of-thought…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. At release, the best models achieve <10% accuracy (GPT 5.2: 9.8%; Gemini 3 Pro: 6.1%) on LongCoT, revealing a substantial gap in current capabilities.…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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Introducing LongCoT, a benchmark for evaluating long-horizon chain-of-thought reasoning in LLMs, revealing a significant gap in current model capabilities and providing a rigorous measure for future progress.
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10.48550/arXiv.2604.14140Introducing LongCoT, a benchmark for evaluating long-horizon chain-of-thought reasoning in LLMs, revealing a significant gap in current model capabilities and providing a rigorous measure for future progress.
Abstract
As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this ability is planning and managing a long, complex chain-of-thought (CoT). We introduce LongCoT, a scalable benchmark of 2,500 expert-designed problems spanning chemistry, mathematics, computer science, chess, and logic to isolate and directly measure the long-horizon CoT reasoning capabilities of frontier models. Problems consist of a short input with a verifiable answer; solving them requires navigating a graph of interdependent steps that span tens to hundreds of thousands of reasoning tokens. Each local step is individually tractable for frontier models, so failures reflect long-horizon reasoning limitations. At release, the best models achieve <10% accuracy (GPT 5.2: 9.8%; Gemini 3 Pro: 6.1%) on LongCoT, revealing a substantial gap in current capabilities. Overall, LongCoT provides a rigorous measure of long-horizon reasoning, tracking the ability of frontier models to reason reliably over extended periods.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
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Dimensions overall score 7.0
PROBLEM
Introducing LongCoT, a benchmark for evaluating long-horizon chain-of-thought reasoning in LLMs, revealing a significant gap in current model capabilities and providing a rigorous measure for future progress. An essential component of this ability is planning and managing a long...
METHOD
As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this ability is planning and managing a long, complex chain-of-thought (CoT).
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. At release, the best models achieve <10% accuracy (GPT 5.2: 9.8%; Gemini 3 Pro: 6.1%) on LongCoT, revealing a substantial gap in current capabilities. Code availability is flagged in the production record...
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Introducing LongCoT, a benchmark for evaluating long-horizon chain-of-thought reasoning in LLMs, revealing a significant gap in current model capabilities and providing a rigorous measure for future progress. An essential component of this ability is planning and managing a long, complex chain-of-thought (CoT).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
As language models are increasingly deployed for complex autonomous tasks, their ability to reason accurately over longer horizons becomes critical. An essential component of this ability is planning and managing a long, complex chain-of-thought (CoT).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. At release, the best models achieve <10% accuracy (GPT 5.2: 9.8%; Gemini 3 Pro: 6.1%) on LongCoT, revealing a substantial gap in current capabilities. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Introducing LongCoT, a benchmark for evaluating long-horizon chain-of-thought reasoning in LLMs, revealing a significant gap in current model capabilities and providing a rigorous measure for future progress.
Segment
LLM Evaluation
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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Integration burden
missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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