GitHub 新星
10 条
arXiv 论文
10 条
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1
Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success
Robotic foundation models are typically evaluated through costly closed-loop rollouts, which motivates cheap offline proxies for model capability. The Platonic Representation Hypothesis (PRH) suggestsYunfei Xie, Howard H. Qian, Shiyi Lan
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2
Direct Pass@$k$ Optimization for Thought Pruning: Balancing Quality and Diversity in Test-Time Reasoning
Test-time scaling has emerged as a key inference strategy for reasoning models, where a model generates a large number of candidate solutions in the hope of finding at least one correct solution. RunnMahdi Salmani, Adel Javanmard, Devansh Gupta
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3
Phase Transition from Random Features to Fully Trainable Networks
Constructing accurate and computationally tractable approximation spaces is a fundamental task in scientific computing. Partially trainable neural networks bridge fixed random features and fully adaptXin He, Huan Lao, Xiliang Lu
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4
Reliable AI-Assisted Decisions via Selective Auditing
We consider online monitoring of an AI system that produces a sequence of decisions. At each round, the system outputs a proposed decision together with a confidence score estimating the trustworthineIsaac Gibbs, Elise L. Han, Ryan J. Tibshirani
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SpectralCanary: Embarrassingly Simple Low-Frequency Traces for T2I Dataset-Use Auditing
Auditing whether a text-to-image (T2I) model was fine-tuned on a particular image collection is difficult: a model can absorb collection-level visual characteristics without reproducing training imageSy-Tuyen Ho, Anirudh Satheesh, Soham Chawathe
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6
Cost-Aware Abstention for LLM-Based IT Ticket Classification
Wrong automatic routing of an IT support ticket can cost more than human triage. We evaluate cost-aware abstention as an offline, cost-conditional readiness assessment: select a routing policy on caliSachin Gupta
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METTA: Memory-Enhanced Test-Time Adaptation for Meta-Reinforcement Learning
Meta-reinforcement learning aims to train agents that adapt quickly to unseen tasks from limited interaction, but this requires representing task evidence over long interaction histories. Classical reAli Najar, Hosein Hasani, Amir Kooshan Fattah
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EpiSEM-Bench: Evaluating Safety and Evidence Recovery in LLM Agents under Missing Evidence
Tool-using language model agents rely on environmental facts to decide whether an action is permitted. When a tool response omits a safety-relevant fact, an agent may carry out a prohibited action eveQijin Zhang, Hanzhen Wang, Yuehan Zhao
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Low-Bit N-Gram Embeddings for Language Models
Many recent open-weight language models use n-gram embedding tables to improve performance. These tables store learned representations of short token sequences, such as pairs or triples of consecutiveStephen Chung, Wenyu Du
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10
A Fix for Softmax and Cross-Entropy
Standard implementations of softmax, log-softmax, and cross-entropy have two numerical bugs. The first bug, Softmax Collapse, rounds the target logit's gradient to zero if that one logit is much largeJonathan T. Barron, Dan Gnanapragasam, Michael Tschannen
Hacker News
10 条
- 1 Root shell on Samsung TV
- 2 We should be more ambitious with the software we build
- 3 A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis
- 4 I refuse to accept a censored Internet. Join Me at FOSDEM 27
- 5 Oregon Police Are Charging a Local News Outlet $2.5M for Records About One Cop
- 6 Dolly Parton's Imagination Library
- 7 Serbia's Vucic concedes election defeat in historic win for student movement
- 8 AI use leads UC Berkeley math professor to see 'no point' in grading homework
- 9 JetBrains Thinkrail
- 10 Why I Am a Programmer