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.
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.
arXiv:2606. 05464v1 Announce Type: new Abstract: Verifiable reward training has improved mathematical and coding reasoning, but these domains capture only part of step-by-step decision making.
arXiv:2509. 16456v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in various domains, showing impressive potential on different tasks.
The paper introduces Agentic Reasoning for Tree Search (ARTS), a method that uses a reasoning language model to navigate the hypothesis‑experiment space in scientific discovery. Unlike traditional approaches that conflate hypothesis quality with execution quality and prune search logs, ARTS evaluates prior execution logs to distinguish implementation failures from poor hypotheses and selects the next hypothesis to pursue. By employing test‑time training to embed search‑tree knowledge into model weights, ARTS achieves a 15.3% relative improvement over leading algorithms on 22 benchmark tasks and enables smaller models like Qwen3‑4B to match or exceed the performance of larger closed‑source models at lower inference cost.
WordPolo is a word‑finding task that evaluates language models by having them guess an unknown target word and receive semantic similarity feedback. Participants start with no knowledge, make iterative guesses, and receive distance scores that guide them through semantic space. The study tests recent LLMs, LRMs, humans, and a heuristic on 1,500 puzzles, revealing that while solve rates vary widely, many models make meaningful progress and exhibit human‑like strategies, highlighting the importance of assessing reasoning processes, not just final accuracy.
arXiv:2605. 03862v4 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards has become a common way to improve explicit reasoning in large language models, but final-answer correctness alone does not reveal whether the reasoning trace is faithful, reliable, or useful to the model that consumes it.
arXiv:2609.16055v1 Announce Type: cross Abstract: Test-time compute has emerged as a major approach to improving the capabilities of Large Language Models (LLMs). However, existing test-time reasonin...
arXiv:2605.13165v2 Announce Type: replace Abstract: Long chain-of-thought (Long CoT) reasoning improves performance on multi-step problems, but it also induces overthinking. This inefficiency is espe...
arXiv:2512. 07795v2 Announce Type: replace Abstract: Benchmark scores for LLM reasoning systems are reported as single numbers, yet the same model, strategy, and task can produce meaningfully different answers and costs across repeated executions, even under greedy decoding (T = 0).
The paper proposes a unified framework for test‑time reasoning methods, framing them as recursion operators—GROW, PRUNE, and BRANCH—applied to an agent’s reasoning trace. Experiments across five benchmarks and three frontier models show that BRANCH, which samples and selects among multiple reasoning paths, consistently outperforms the other operators and a single‑pass chain‑of‑thought baseline, improving accuracy by an average of 5.98 percentage points. The study also highlights the importance of paired evaluation and careful handling of scoring‑pipeline failures, as these factors can significantly alter comparative outcomes.
arXiv:2510. 19990v2 Announce Type: replace Abstract: The reasoning paradigm, where language models reason before answering, has enabled breakthroughs on tasks such as mathematical problem-solving.
arXiv:2609.21492v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely r...