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Newest papers, page 164
Learning to Reason Efficiently with Interventional Step Credit
Large reasoning models often produce lengthy chain-of-thought (CoT) traces mixing useful intermediate steps with unnecessary ones.DeltaTrace: Efficient Signed Attribution for Reasoning Language Models
We study how words in the context support or oppose a language model's response and present DeltaTrace, a novel attribution framework that explains the effect of input tokens on a fixed model…Asynchronous On-Policy Self-Distillation
Unlike on-policy distillation (OPD), which depends on a separate teacher model, on-policy self-distillation (OPSD) leverages a single language model acting simultaneously as the student and the…Learn the Interface, Not the Implementation: Output Representation Dominates Training Regime in LLM-Based RTL Generation
Large-language-model register-transfer-level (RTL) generation has concentrated on how to train the model: fine-tuning, continued pre-training, and reinforcement learning from a verifier.Motor compound fault diagnosis using an attention-based cross-talk architecture
As physical AI increasingly relies on sensing to understand physical systems, reliable condition monitoring of industrial machinery has become more important.Out-of-Distribution Detection in Continual Learning
Out-of-distribution detection in continual learning (OOD-CL) is highly challenging, as it requires (i) learning a sequence of tasks without catastrophic forgetting, and (ii) identifying unknown…An Analysis of Semantic and Spatio-Temporal Co-evolution for Edge Classification on Dynamic Text-Attributed Graphs
Dynamic edge classification on text-attributed Web graphs, such as review networks, community forums, and news-derived knowledge graphs, requires high quality, adaptive semantic representations in…Maestro: Agentic Formal Proof Development by Coordination
Lean is increasingly used to verify mathematical proofs and software produced by LLM agents, but writing formal proofs remains hard.UtilityBench: Measuring Language Model Utility When Errors Have Different Costs
As language models are increasingly deployed in real-world scenarios with important outcomes, the impact of their mistakes often extends far beyond what correctness can capture.EvoSkill v2: Fully Agentic Skill Evolution from Benchmark Adaptation to a Stateful Evolver
EvoSkill v2 is a three-agent system that edits the instructions (skills) an agent is given, leaving its model and harness intact.Keep the Lineages Alive: Diversity-Preserving SMC for Diffusion LLM Decoding
Inference-time sequential Monte Carlo (SMC) decoding can improve the reasoning accuracy of masked diffusion language models (diffusion LLMs) without post-training by resampling a population of…The KV-Surgeon: Compensation-Aware Offline KV-Cache Compaction
Offline compaction of the key--value cache (KV-cache) reduces the cost of repeatedly querying a fixed context by constructing a smaller cache once and reusing it subsequently.MITRA-lexicon: Lexica of attested translations from Sanskrit into English, Tibetan and Chinese
The standard Sanskrit lexica are monumental and indispensable, but they cite their attestations as abbreviated references to editions, without links to the digitized texts now widely available and…OrderGPT: Generation Order as a Learnable Modeling Choice
Generative modeling of discrete data has two popular paradigms: autoregressive models such as GPT, which factorize a sequence in a left-to-right (L2R) order, and masked diffusion models, which are…Productive State-Space Exploration for Multi-turn RL in LLM Agents
Reinforcement Learning (RL) for Large Language Model (LLM) agents in multi-turn, long-horizon environments remains challenging due to inefficient action selection and repetitive behavioral patterns.Correctness-Dependent Asymmetric Target Distributions for Self-Distillation Policy Optimization
Self-distillation policy optimization provides dense token-level supervision for LLM post-training through feedback-conditioned self-teachers.NIOCIS: Typed-Decision Next-Hop Routing with Node-Conditioned Option Spaces for Tool-Use Agents
Tool-use agents pay per decision: at each hop a language model re-reads the state and thinks for seconds, though its actions are finite and decisions recur.Graph-to-Table Interfaces Control Relational Generalisation
Graph-to-table systems combine three design choices: an encoder that maps relational evidence to feature rows, a context constructor that selects labelled examples, and a tabular decoder.Federated Community Detection in Bipartite Networks
We study federated spectral estimation when a large matrix is horizontally partitioned across servers that cannot share raw data.Internalizing Chain-of-thought into Looped Transformers
Chain-of-thought (CoT) enables constant-bit-size transformers to perform general-purpose computation through generated tokens (Li and Wang, 2025a), whereas looped transformers carry out the same…