Hugging Face Trending Papers
Escaping Redundant Reasoning: Structure-Aware Search for Inference-Time LLMs
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arXiv AI
Sep 2
Escaping Redundant Reasoning: Structure-Aware Search for Inference-Time LLMs
The paper introduces BASIN, a training‑free, structure‑aware selection method that groups reasoning states into basins and penalizes repeated visits to the same strategy, thereby redistributing search across distinct reasoning paths within a fixed compute budget. BASIN outperforms the Tree of Thoughts (ToT) baseline by up to +22 percentage points on Game of 24 and +6.7 percentage points on MuSR, and its quality‑aware variant QA‑BASIN further enhances robustness by preserving high‑quality basins. The authors also define a redundancy gap metric, Δ, to quantify how search concentrates differently for correct versus incorrect predictions, showing that ToT often operates near Δ ≈ 0 while BASIN consistently shifts Δ positive.
arXiv AI
Jun 15
Fractured Chain-of-Thought Reasoning
arXiv:2505. 12992v4 Announce Type: replace-cross Abstract: Inference-time scaling techniques have significantly bolstered the reasoning capabilities of large language models (LLMs) by harnessing additional computational effort at inference without retraining.
arXiv AI
Jun 17
Know Thy Reasoner: Not All Language Models Explore Alike
arXiv:2604. 10827v2 Announce Type: replace Abstract: Compute scaling for LLM reasoning trades off exploring solution approaches (\emph{breadth}) against refining promising ones (\emph{depth}), yet why a given trade-off works, and why it often fails to transfer across models, remains unclear.
arXiv Computation and Language
Sep 1
When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Models
arXiv:2608.30395v1 Announce Type: new Abstract: As pretraining scaling laws approach saturation, Test-Time Scaling (TTS) has emerged as an important direction for improving reasoning by allocating in...
arXiv Computation and Language
Sep 14
Chopthin-Consensus Power Sampling: A Diversity-Preserving Approach to LLM Decoding
Chopthin-Consensus Power Sampling (CCPS) is a new inference-time decoding method for large language models that uses the Chopthin resampler to preserve diversity among particle trajectories. By enforcing an upper bound on weight ratios instead of equal-weight resampling, CCPS maintains a richer set of distinct reasoning paths and guarantees a lower bound on effective sample size. Coupled with a semantic-majority selection mechanism, CCPS achieves higher oracle coverage and matches or surpasses baseline accuracy on multiple reasoning benchmarks.
arXiv AI
Sep 7
Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models
The paper surveys efficient reasoning in large language models, contrasting fast intuitive (System 1) and slow deep (System 2) reasoning. It analyzes why System 2 is computationally costly yet more accurate, and why System 1 is efficient but less effective. The survey covers causes of inefficiency, patterns of reasoning behavior, and potential solutions to balance performance and computational budgets, offering actionable insights and an open‑source repository for ongoing research.