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Computation & Language / NLP
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CoRD: Coupled Rollouts Fill the Blind Spot of On-Policy Distillation
On-policy distillation trains a student on its own rollouts, so states the teacher would reach but the student does not are seldom supervised.TCSExam: Evaluating Frontier LLMs on the Full Range of TCS Research
Can large language models (LLMs) advance theoretical computer science and resolve its long-standing open conjectures?How Far Can Lightweight LLMs Go in Intent-to-SLO Translation?
Intent-based networking (IBN) promises QoS provisioning in which the network derives the configuration from operator-stated goals.Compression Through a Context Lens: Evaluating Adaptation, Quantization, and Distillation in Arabic Legal LLMs
Model compression can facilitate the deployment of large language models in resource-constrained legal settings, but its effects on domain-specific performance may depend on the evaluation protocol.Automatic keyphrase extraction and annotation: modern theoretical approaches and practical solutions for text and speech
The exponential growth of textual and audiovisual information has made the task of automatic keyphrase extraction (KE) increasingly significant.Learning Dynamics of On-Policy Distillation
On-policy distillation (OPD) is emerging as an important paradigm for language-model post-training, combining dense teacher supervision with training on student-generated trajectories.Linguistic boundaries emerge from pressure-constrained processing dynamics
Human cognition organizes continuous experience into bounded units while maintaining continuity across them.ATHAAR: A Medieval Scientific Arabic Manuscript Dataset for Handwritten Text Recognition
Despite recent progress in handwritten text recognition, medieval Arabic scientific manuscripts remain an open challenge for current vision-language models (VLMs) due to cursive ligatures, variable…Gabor Gabor: E-Gaborformer, a Strictly Causal Speech Recognizer with Gabor-Dictionary Convolutions and Delta-Rule Memory
Real-time voice agents need a speech recognizer that never waits for future audio and whose memory per call does not grow with the length of the call.Beyond K/V Caches: Cross-Model State Handoff in Hybrid Linear-Attention LLMs
Production deployments often use different models to balance cost and quality through cascading, routing, and switching models during long conversations.RouteAlign: Aligning Task Requirements with Model Profiles for LLM Service Selection
LLM service selection requires performance evidence that reflects the demands of the current task.Knowing When to Teach: Learning from Teachable Prefixes in Multilingual Reasoning Distillation
On-Policy Self-Distillation (OPSD) improves reasoning by training a student on its own reasoning, using the same model conditioned on reference solutions as a teacher.SMART: State-based Multi Agent Reasoning Traces for Verification-oriented Problem-Solving
Complex problems with limited context, such as claims, require a means to obtain additional knowledge necessary for solution reasoning.ProQuant: Progressive Quantization-aware Training for Edge MLLMs
Multi-modal large language models (MLLMs) with impressive perception and understanding capabilities will enable a range of downstream applications.Agent-R$^2$: Scaling Agents for Realistic Productivity Work via Real-Source Data Synthesis
Large language models have made rapid progress in agentic reasoning and tool use, raising expectations for agents that can take over realistic productivity work from humans."Blind Compliance": Coercive Control in AI Jailbreaking
The explosion of generative AI systems and concerns surrounding their rapidly scaling capabilities has led to an adversarial cycle between increasingly narrow content restrictions ("guardrails") and…From Twisted Facts to Rogue Agents: Can LLMs Reliably Detect Multi-Dimensional Hallucinations?
Large language models (LLMs) are widely used for information access, content production, and agent-based task execution, but their outputs remain vulnerable to subtle, high-risk hallucinations.Terminal-style Mathematical Modeling Bench: The First Agentic and Verifiable Benchmark for Mathematical Modeling
Mathematical modeling turns an open-ended real-world problem into assumptions, a model, a solution, and a defended recommendation, and its answers matter only if they are both close to a credible…The Geometry of Simulated Perspectives: Dissociating Representation and Behaviour in a Multimodal Language Model
Multimodal language models can simulate different observers, yet it remains unclear how assigned perspectives reshape their internal representations and whether these changes govern their judgements.Multimodal-SEA: Benchmarking AI Agent Security under Multimodal Social Engineering Attacks
Personal AI agents face social engineering attacks supported by forged multimodal content, but existing evaluations mainly focus on text-based dialogue, leaving their intrinsic security under such…