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Newest papers, page 238
Can LLMs Reason About Runtime Behavior? A Repository-Level Dynamic Benchmark
Large language models (LLMs) are increasingly used in coding tasks, but their ability to reason about code execution remains unclear.Contrastive Learning for Authorship Verification
Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings.When Explanations Cannot Be Read: Measuring and Correcting SHAP and LIME Rendering for Right-to-Left Languages
Post hoc explanation methods such as SHAP and LIME are widely used to interpret text classifiers, but their visualizations are mainly designed for left-to-right languages.Reward Hacking Challenges Oversight of Autonomous Research Agents
Autonomous research agents can design experiments, evaluate results, and write reports, giving them control over both a scientific result and the evidence used to support it.The Fellowship of the Query: Learning Retrieval Actions
Retrieval-augmented question answering requires control decisions about when to decompose a question, search, reformulate, extract evidence, synthesize facts, verify progress, and stop.Benchmarking Argumentative Behaviour of LLMs: A Study of Defences Against Character Attacks
Large Language Models (LLMs) are increasingly deployed as argumentative agents in persuasive dialogues, necessitating rigorous evaluation of their debating competence relative to human interlocutors.An Explainable DistilBERT-BiLSTM-Attention Framework for Binary and Multi-Class Hate Speech Detection
Hate speech on social media poses serious risks to social harmony, mental well-being, and public safety, making its timely and accurate detection essential for content moderation systems.Spooftral: Can Voxtral Audio-Language Model Detect Speech Spoofing?
Self-supervised learning (SSL) countermeasures (CMs) have shown strong performance in recent years.PTC-Bias: Phoneme-Level Temporal Competition for Bias Retrieval and Post-Decoding Correction in Speech LLMs
Contextual biasing improves rare-word recognition in speech large language models (SpeechLLMs), but efficiently exploiting large bias lists remains challenging.Temporal Taxation Compounds Under Post-Training Compression of Whisper Models
Automatic speech recognition models are audited for demographic fairness at full precision, yet the models that ship to production have been quantized, pruned, and distilled.Technical Manual for Toolkit for Confidence-Corpus Consistency via Fine-Tuning on a Fabricated Corpus
A language model's confidence in an answer is often read as a proxy for how well it knows the corresponding fact.Small yet Assistive: Spatially-Aware Post-Training for Low Vision
An estimated 1 billion people worldwide live with vision impairment, yet current vision-language models (VLMs) produce descriptions too vague for safe navigation by blind and low-vision (BLV) users.BiMamba2 Masked Discrete-Unit Prediction for Multilingual Speech Representation for Unsupervised Speech in the Wild Challenge
We describe our submission to the Unsupervised Speech in the Wild (UPS) Challenge at Interspeech 2026, a bidirectional Mamba-2 (BiMamba2) encoder trained with masked discrete-unit prediction…Reward-Tilted On-Policy Distillation for Acoustic Grounding in Audio-Language Models
Audio-language models (ALMs) can exploit textual shortcuts to answer questions while overlooking acoustic evidence, weakening audio understanding.Script Choice in LLMs: Evidence for Late-Layer Commitment
In this paper, we investigate how script knowledge is distributed across the layers of LLMs using two complementary interpretability methods: logistic regression probing and logit-lens analysis.COILD: An Indic-Centric Parallel Corpus and Benchmark for Machine Translation Across Indian Languages
Machine translation (MT) for Indian languages remains constrained by the limited availability of high-quality, Indic-centric parallel corpora and evaluation benchmarks.LastOPD: Taming Collapse in Latent On-Policy Distillation
On-policy distillation (OPD) corrects a student on the responses it writes, but its signal is the teacher's next-token distribution: it tells the student what the teacher says but misses how it…Persuaded, Not Informed: Incentive-Misaligned Witnesses Defeat In-Context Grounding
Language-model agents increasingly answer questions over customer-relationship management (CRM) records, such as whether to qualify a sales lead.When Is a Multi-Agent Code Judge Actually Grounded? Two Label-Free Measurements, and a Judge That Declines to Guess
When one language model judges whether another's code is correct, it does not report the absence of evidence.When Should LLMs Trust Their Own Revisions? A Risk-Aware Study of Intrinsic Self-Correction
Intrinsic self-correction asks a language model to revise its own answer without receiving new external evidence.