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Computation & Language / NLP
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The newest 5,000 papers, from 18 September 2026 to 6 November 2026: 5,000 arXiv preprints.
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Newest papers, page 203
EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making
Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments.OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime
Multi-agent coding systems are designed to tackle complex software engineering tasks through collaboration.Halluscoring 2026: The first shared task on llms hallucination detection and answer verification
We present HalluScoring 2026, a shared task for evaluating hallucination detection and factual verification in Arabic question answering under challenging generalization settings.Evaluating Language Model Safety Across Long Adversarial Conversations
Conversational safety evaluations often test language models with a single harmful prompt, even though real-world systems interact with users through long, adaptive conversations.Beyond Mode Collapse: Generating Diverse Synthetic Expert Conversations via Generative Flow Networks
High quality synthetic data is central to post training LLMs for adaptive AI applications that represent the diverse expert strategies and decisions in conversations.On the Off-Policy Teacher in On-Policy Distillation
On-policy distillation (OPD) has recently emerged as a promising post-training paradigm in which the student learns from trajectories generated by its own policy under dense teacher supervision.TALK-Dem: Benchmarking Embodied Task Planning under Dementia-Associated Communication Patterns
Existing LLM-driven robot task planners rely on a taken-for-granted assumption of an ideal user whose instructions are clear, complete, and task-focused.Doc2LoRA Provides Decodable Representations of Scientific Ideas
Representing scientific papers as points in a space lets us search for similar papers and inquire about how fields relate to one another and drive innovation.Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting
Supervised fine-tuning of discrete diffusion language models masks some response tokens and trains the model to recover their original values from the visible context.The Geometry of Harmfulness in Multi-Turn Attacks
Large language models (LLMs) remain vulnerable to adversarial attacks that circumvent safety alignment to elicit harmful outputs.Evaluating Whether LLMs Can Reliably Connect the DOTs?
Access to real-world information is often noisy and fragmented.ArgGYM: A Procedural, Engine-Verified Benchmark for Structured Defeasible Reasoning
Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable rewards, particularly in mathematics, code, and…What Was Said, Not What Was 'Thought': Type-6 Logic for CoT Verification
We introduce Type-6 logic, a variant of dynamic epistemic logic augmented with two operators (uncertainty and recurrence), designed to model the inferential dynamics of contemporary large language…LoopVL: Recurrent Visual Intelligence
We introduce LoopVL to study whether Loop Transformers can be effectively extended to vision- language models.Policy-Conditioned AI-Use Detection: An Evidentiary Framework for Academic Publishing
Major venues now publish detailed rules about how authors, reviewers, and area chairs may use AI, and those rules differ by role, by task, and by what must be disclosed.What Pretraining and Midtraining Make Learnable from Rewards?
A reward can identify a correct answer while leaving the computation needed for new inputs undetermined.Reach Into The CHOIR: Free-List Elicitation Uncovers Distinct Model Voices in LLM Ensembles
Open-ended LLM homogeneity can create false plurality when several systems appear to offer independent perspectives while returning the same familiar default.The Backdrop Exposes What the World Around an Agent Costs It
Agent benchmarks test agents in worlds that stay still.KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy
Most clinical benchmarks evaluate language models (LMs) on diagnosis using complete case descriptions.Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations
Large Language Models (LLMs) and more broadly Artificial Intelligence (AI) systems are often described and understood in human-like terms, a phenomenon known as anthropomorphism.