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Computation & Language / NLP
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TeCoP: Accelerating Diffusion Large Language Model Decoding via Tentative Context Pipelining
Diffusion large language models (dLLMs) predict multiple token positions per forward pass, yet block-wise decoders serialize generation and repeatedly recompute stable predictions.Beyond Model Weights: Sampling-Dependent Fairness in Diffusion Language Models
Diffusion language models (DLMs) are emerging as a practical alternative to autoregressive language models (ARLMs), yet their fairness remains largely uncharacterized.Benefit and Cost of Random-Token Replacement under Repeated Data: A Known-Entropy Decomposition and Evidence from Ten Real Corpora
When unique pretraining data is scarce, models see the same tokens many times and are thought to memorize.WORTH Benchmark: Evidence Discovery under Not-Fully-Cooperative Information -- A Two-Episode Pilot
Existing reasoning evaluations typically provide task-relevant information to models as available premises, rarely examining concurrently how models discover evidence autonomously and how…Self-Decomposing Diffusion for Code Generation
Diffusion language models (DLMs) are emerging as a promising paradigm for code generation, offering the potential for fast, parallel decoding.Auditing the Product, Not the Recursion: The Evaluation Evidence Behind Recursive Self-Improvement Claims
Recursive self-improvement (RSI) promises AI systems, such as self-evolving LLM agents, that become better at producing further improvements.ABench: Adversarily Benchmarking Large Language Models across Taxonomized Values
Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment.PAR: Progress-Aligned Recurrence for Length Generalization in State Tracking
Looped language models can run more iterations at test time, but additional iterations need not help them solve longer problems.A Two-Way Street: Surveying the Interplay between Code-Mixing and Native Languages in Text and Speech
Systems for code-mixed (CM) text and speech rarely start from scratch.BCT: Bayes-Consistent Tempering for Flow Language Models
Temperature scaling is typically applied as a post-hoc transformation of model probabilities.CUA-Recovery: Benchmarking Error Recovery in Long-Horizon Computer-Use Agents
As computer-use agents (CUAs) move to long-horizon workflows spanning multiple applications, undetected errors can silently corrupt persistent state and cause cascading failures in subsequent actions.Speak vs. Know: Balancing Behavior and Knowledge Distributions of Large Language Models
Recent work reports that the distribution verbalized directly in the output text aligns far better with human opinion distributions than the next-token log-probability distribution of LLMs.Corpus Quality Improves, Retrieval Utility Does Not: An Ablation of Preprocessing and Refinement in a Speech-to-QA Pipeline
Practitioners building training corpora with a pipeline of language models keep a preprocessing or refinement (post-process) stage when switching it on raises the corpus quality.CovNav: Conformal Prediction-Guided Adaptive Inference for Object Goal Navigation
In object goal navigation (ObjectNav), an embodied agent must find a target object (e.g., "find a bed") in a previously unseen environment, relying on spatial commonsense such as object co-occurrence.PhonoBridge: Error-Aware Phoneme-Mediated EMG-to-Text Reconstruction
Electromyography (EMG) provides a non-invasive means of decoding silently articulated speech into text, but accurate decoding remains challenging due to limited availability of paired EMG-text data.Memory as Code: Declarative Retrieval over Codified Long-Term Memory
In long-term memory systems, memories are constructed from interaction histories and retrieved in response to queries, enhancing the reasoning ability of LLM agents.Beyond Calibration: Temporal LLM Confidence Assessment Across Languages and Local Settings
Large language models are expected to estimate their own uncertainty, with confidence signals used to guide retrieval, adaptive compute allocation, routing, and selective abstention.When Does Experience Memory Help Closed-Loop LLM Driving Agents? A Controlled Evaluation with Resampled Warmup Pools, Token-Matched Fillers, and Audited Baselines
When does experience memory help a closed-loop LLM driving agent?Tracing Recurrent and Emergent Hallucination Patterns in Decomposed Text-to-SQL Generation
Large language models can achieve strong Text-to-SQL benchmark performance while still producing schema and query-logic errors.Hearsay or Fact? How Framing and Elicitation Shape LLM Probability Updates
Large language models (LLMs) are increasingly used as judgment assistants and as stand-in participants in psychology experiments, yet little is known about how much their probability estimates move…