arXiv AI By Guni Sharon

Tree of Thoughts as a Classical Heuristic Search Problem: Formal Foundations and Design Patterns

Read the original on arXiv AI →

arXiv:2605. 28566v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, yet their standard generation process -- auto-regressive token prediction -- is inherently myopic and prone to cascading errors.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language
Aug 24

Self-Speculation for Faster Reasoning Models

arXiv:2608.20359v1 Announce Type: new Abstract: Large language models (LLMs) are deployed for increasingly complex tasks involving planning and multi-step decision making, but high-quality performanc...

By Ravisri Valluri, Tung Nguyen, Aditya Grover
arXiv AI
Aug 14

TsuGO: Probing Search Efficiency in LLM Reasoning via Go Life-and-Death Problems

arXiv:2608. 13221v1 Announce Type: new Abstract: The evaluation of LLM reasoning is moving from final-answer accuracy to process-level assessment, yet existing methods still fail to capture how models plan reasoning paths and allocate reasoning resources--that is, how they organize search.

By Shunwen Bai, Ziping Ma, Chaoyang Zhang, Yarong Wang, Jiale Liu, Zhen Qin, Qingpei Guo
Hugging Face Trending Papers
5d ago

Not All Thinking is Created Equal: Latent Reasoning Discovers a Recurrent Search Algorithm for Depth Generalization

The paper investigates whether different forms of intermediate computation in large language models—such as token-based traces, pause tokens, and latent reasoning—rely on the same underlying mechanism. By training five variants of GPTNeoX on an extended multi-hop reasoning task, the authors find that while vanilla, Chain-of-Thought, and Pause Token models perform well on in-distribution data, they fail to generalize to longer-hop out-of-distribution problems. In contrast, latent-reasoning models exhibit better depth generalization, with causal analysis revealing a sparse recurrent search circuit that implements forward reachability propagation across the graph.