Chain of thought

Evidence in the last complete seven days

Source mentions

6

Previous seven days 2

Distinct sources

1

Previous 1

Comparable growth

Not comparable

Ranking appearances

0

Collection observations and undated records are separate from dated publication counts. Observed in 7d：2 · Undated records：0

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

Source evidence

Sources in the evidence shown：arXiv 5 · HF Daily Papers 3

1. arXiv · Published Nov 4, 2026

   Low-Rank Hierarchical Merging for Efficient Long-to-Short Reasoning

   arXiv:2611.03834v1 Announce Type: new Abstract: Long-to-Short (L2S) model merging seeks to combine the strong reasoning capability of long chain-of-thought models with the concise style of base models, but existing methods that rely on linear interpolation and first-order activation statistics fail to capture how the loss landscape responds to parameter disp

   View source record →

2. HF Daily Papers · Published Nov 3, 2026

   LoopVQ: Sub 1-bit KV Cache Quantization for Looped Transformers

   Looped transformers are a parameter-efficient alternative to standard transformers for language modeling. By reapplying the same layers across multiple loop steps, recent looped language models match the performance of non-looped models several times their size. While this architecture effectively reduces parameter memory, each loop step still stores its own

3. HF Daily Papers · Published Nov 3, 2026

   Think Less, Locally: Local Preference Learning for Reasoning Compression

   Large reasoning models achieve strong performance on challenging tasks, but their long chain-of-thought often incurs substantial inference costs by continuing to deliberate after sufficient information has been obtained. Existing reasoning compression methods primarily operate on reasoning trajectories, paying less attention to the local decision of whether

4. arXiv · Published Nov 2, 2026

   The First Few Tokens Matter: Understanding Chain-of-Thought Controllability

   arXiv:2610.40939v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising way to oversee increasingly capable agents. It succeeds in part because current models appear limited in their ability to control what they verbalize in their reasoning traces. We investigate the source of this limitation and link it to an entropy collapse in the

   View source record →

5. arXiv · Published Nov 2, 2026

   Opening the Box Changes the Cat: LLMs Detect Chain-of-Thought Monitoring - What now?

   arXiv:2610.40946v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning allows large language models (LLMs) to generate intermediate reasoning traces in natural language while solving complex tasks. These traces can support safety monitoring by exposing potentially harmful plans before actions are executed. In this work, we investigate the condition

   View source record →

6. HF Daily Papers · Published Nov 1, 2026

   TAC-CoT: Tool-Empowered Implicit Chain-of-Thought via Interleaving Explicit Tokens

   LLM-based agentic systems are increasingly combined with CoT reasoning to solve tasks that require accurate reasoning, external knowledge, and multi-step deliberation. Yet, explicit CoT incurs substantial generation overhead, while implicit CoT, which replaces textual rationales with compact continuous tokens, does not naturally support the explicit communic

7. arXiv · Published Oct 23, 2026

   Speculative Acceptance Does Not Decay with Position: A Full-Length Diagnostic on Long Chain-of-Thought with Independent Drafts

   arXiv:2610.30644v1 Announce Type: new Abstract: The claim that speculative-decoding acceptance rate decays as a chain-of-thought grows longer has become a de facto industry narrative, motivating draft-model co-design and position-aware training. Nearly all direct evidence comes from trained drafters whose offline training length is far shorter than inference

   View source record →

8. arXiv · Published Oct 22, 2026

   A Large-Scale Evaluation of Role-Enhanced Zero-Shot Chain-of-Thought Prompting

   arXiv:2610.29073v1 Announce Type: new Abstract: This paper proposes Role-Enhanced Zero-Shot Chain-of-Thought (RE-ZS-CoT), a prompting framework that systematically integrates domain-specific expert roles to enhance reasoning in large language models (LLMs) without task-specific examples. We assess two versions of our approach. In the first version, static ro

   View source record →

Review priority 55 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

• Evidence

  22.5 / 30

  Distinct dated articles or first observations; repeat crawling adds nothing.

• Publisher diversity

  8 / 25

  Conservative owner grouping; syndicated copies count once.

• Comparable change

  0 / 20

  Comparable complete weeks only; no percentage from a zero baseline.

• Recent evidence

  15 / 15

  Latest publication or first observation, not repeated fetch time.

• Name specificity

  9.7 / 10

  Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

• First observation is a collection milestone, not proof that a word or product is new to the world.
• Counts describe monitored public sources, not the whole web or search demand.
• First observed here is not the invention date of a name or idea.
• Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
• Owner grouping is conservative; undisclosed syndication may remain.
• Repeat ranking appearances are observations, not new articles or independent endorsements.
• An established concept or common product name retained as a baseline, not a newly invented term.
• Both weeks need adequate, comparable collection coverage.
• Sources behind this term changed or lack a comparable baseline.
• Recent evidence includes collection over three days after publication.
• Current evidence comes from one publisher or owner.