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Tuesday 20 October
- PaperFrom DQI to C-DQI: Automatic Codebook Generation and Evaluation for Measuring Computational Discourse Quality Index
Effective democratic deliberation is vital for civic engagement and the health of legislative institutions. However, assessing parliamentary discourse quality is traditio
- PaperFrom Comparison to Selection: Enhancing Cross-Scale Generalization of Pointer Networks Trained with Reinforcement Learning for Capacitated Drone Delivery Routing
Unmanned aerial vehicles (UAVs) enable fast, flexible last-mile and emergency delivery, but their routing remains challenging as the number of clients varies. Classical a
- PaperWhy Do LLMs Accept Generics? Explaining Acceptability using Psycholinguistic and Model-Derived Factors
Psycholinguistic studies explore why humans accept some generic statements but reject others by showing that these judgments depend on more than how common a property is.
- PaperQuantum-JEPA: Quantum Born Priors for Latent-Action JEPA World Models
A single observation can admit several plausible futures, making uncertainty an essential part of world modelling. However, quantum generative models remain underexplored
- PaperWorld Models with Configurable Perception
Real-world observations are noisy and often contain excessive task-irrelevant information. Effective world modeling and planning need to abstract observations into world
- PaperACE-Compression: An Ability-Centric Evaluation of Compressed Large Language Models
Model compression makes large language models deployable, but today's evaluation tells an incomplete story. Standard practice measures retained task performance and stops
- PaperICIBS: Increasing the Context for Ice Boundary Segmentation
Mountain glaciers and ice sheets are undergoing rapid loss of ice mass. To assess their current volume and ice-thickness distribution, and to accurately forecast their fu
- PaperMMEnterprise-Bench: Evaluating Enterprise Agents over Evolving Multimodal Records
Enterprise agents answer questions over workplace records that evolve through team collaboration. As information is shared, corrected, and revised across documents, chats
- PaperPolarity Blindness in LLM Safety: A Systematic Study of Negation, Antonym Substitution, and Adversarial Framing
Negation remains a persistent challenge for language models across NLP tasks and modalities. We show that this weakness can undermine LLM safety guardrails through a stru
- PaperStaged Continual Adaptation of Multimodal Foundation Models for Japanese Financial Documents
Staged post-training of multimodal foundation models chains vision-language alignment, reasoning distillation, and domain tuning, yet how each phase trades capabilities a
- PaperCollabSciReM: Aligning Language Models for Scientific Conversations via Science-Aware Reward Decomposition
Large Language Models (LLMs) are increasingly used as research collaborators for hypothesis refinement, methodological planning, and technical analysis across multi-turn
- PaperThe Shape of the DPO Hyperparameter Landscape: What to Measure, How It Moves with Scale, and When Not to Transfer It
Direct preference optimisation (DPO) is widely used for language model post-training, but its performance depends strongly on the choice of its hyperparameters, such as t
- PaperTesting Associative Creativity in Humans and Multimodal LLMs with Difficult-to-Name Visual Stimuli
We investigate associative creativity in descriptions of abstract visual stimuli by humans and multimodal large language models (MLLMs). While conceptual association is c
- PaperPhysical-Variable-Informed Deep Learning for Robust Diagnosis in Industrial Fluid Transport Systems
Industrial fluid transport systems are monitored through heterogeneous sensing streams, but robust fault diagnosis remains difficult because response signals vary with op
- PaperEndoWorld-JEPA: Motion-Guided Global-Local Latent World Modeling for Endoscopic Video Representation Learning
Endoscopic video contains state transitions induced by camera displacement, deformable tissue, and instrument interaction, yet existing pre-training methods treat dynamic
- PaperElicitation or Expansion? Demystifying Gains from Agent Harness Optimization
Recent studies in harness optimization have shown substantial performance gains in language model agents without updating model weights. A well-designed harness more effe
- PaperAutomatic Differentiation with Constraints in Forward and Inverse Problems
Approximate solutions of partial differential equations (PDEs) on unstructured grids can be represented on a graph. Numerical optimization involving stiff evolutionary an
- PaperSGLDet: Spectral-aware Global-Local Dynamic Modeling for Multispectral Aerial Object Detection
Multispectral aerial object detection aims to exploit spectral-spatial cues to address object detection challenges in complex aerial scenes. Existing methods mainly focus
- PaperS$^3$E-Det: A Strict Spatial-Spectral Equivariant Detector for Multispectral Object Detection
Multispectral object detection requires detectors to exploit material-dependent spectral responses while maintaining consistent localization under geometric variations. E
- PaperGaze2HOI: Gaze-Aware Intention Disambiguation for Egocentric Hand-Object Interaction Forecasting
Anticipating how users will interact with their surroundings is essential for proactive systems that provide timely assistance in AR, VR, and embodied applications. Prior
- PaperManifold Prior Guided Deep Unfolding for Hyperspectral Image Reconstruction
Hyperspectral Image (HSI) reconstruction has made substantial progress with the deep unfolding framework by decomposing the problem into a projection module and a denoise
- PaperEnumeration-Guided Training of Neural Hamilton-Jacobi Safety Certificates
Hamilton-Jacobi (HJ) reachability analysis offers formal safety guarantees for control systems, but training accurate neural certificates remains difficult, particularly
- PaperHallucinations of Large Language Models in Cross-Language Code Translation: Phenomena and Mitigation
Code translation converts source code from one programming language to another while preserving its functionality. Although Large Language Models (LLMs) have improved cod
- PaperVISH-ASR: Viterbi-Guided Selection of Hotwords for Speech-LLM Contextual ASR
Prompt-based contextual ASR requires selecting a compact hotword subset from large dynamic inventories, yet existing pre-selection methods rely either on discrete ASR hyp
- PaperFrom Tokens to Files: Multi-Granularity Detection and Localization of AI-Involved Code
The increasing use of large language models for code generation calls for methods that distinguish purely human-written code from AI-involved code: files in which any par
- PaperPhysical Operating-Condition-Guided Vision-Language Matching for Cavitation Diagnosis in Industrial Pipeline Systems
Cavitation diagnosis in industrial pipeline systems is challenging because acoustic responses vary substantially with operating conditions, while similar time-frequency p
- PaperUvA-MT's Participation in the WMT26 General Translation Shared Task
This paper presents UvA-MT's submission to the WMT 2026 General Machine Translation shared task, competing in the unconstrained track across all 23 translation directions
- PaperLaLa-World: Scaling Latent World Models with Natural Language Actions
This paper shows that JEPA-style latent world models (LWMs) can learn across diverse domains and data sources by adopting the language-conditioned interface of video gene
- PaperSAPD: Learning from Agent Experience without Environment Interaction
Scaling the training of Large Language Model (LLM) agents typically requires a large collection of executable environments, whose construction, maintenance, and operation
- PaperSciDiagBench: A Type-Specific Benchmark for Human-Aligned Evaluation of Scientific Diagrams
Recent agents have demonstrated strong capabilities in generating images from textual prompts, motivating their utilization for scientific diagram generation. Scientific
- PaperFrom Generic to Dedicated: A Novel Optimizer for Online Continual Learning
Online Continual Learning (OCL) requires models to learn from non-stationary data streams under a strict single-pass constraint, making them highly susceptible to catastr
- PaperGARRET: Generative Feedback for Region Retrieval in Visual Documents
Multimodal retrieval-augmented generation over visually rich documents requires evidence at a finer granularity than entire pages. Existing visual retrievers typically in
- PaperCOMPASS: Routing via Capability-Oriented Model Profiling with Adaptive Search, Selection, and Tool Use
LLM routing aims to select a capable model at low cost, but specialized routers can require updates as models and tasks change. We propose COMPASS, an agentic router in w
- PaperSMART: Semantic Segment Mapping and Assessment of Recognition Transcripts with Contextual Rubrics
Word error rate (WER) assigns equal cost to lexical edits despite their different semantic consequences, while overall semantic scores cannot identify which individual er
- PaperPrivacy-preserving inference on hosted models: a learned input-embedding transform for frozen Qwen3.8-27B
Every prompt sent to a hosted language model is readable by whoever runs it. We study whether the prompt can remain on the client while the host receives transformed vect
- PaperRAG-NIMBF: A Non-Invasive, Modular Benchmarking Framework for Automated RAG Configuration Analysis
Retrieval-Augmented Generation (RAG) has become the dominant paradigm for grounding the outputs of large language models (LLM) in external knowledge. However, practitione
- PaperProVe: Proposer-Verifier Decoding
Speculative decoding accelerates autoregressive (AR) language models by letting a small model draft tokens serially and a large model verify them in parallel. We reverse
- PaperLatBridge: Efficient and Stable Model Replacement in Latent-Communication Multi-Agent Systems
Multi-agent systems rely on communication to complete tasks, and latent communication passes continuous representations to reduce text generation and re-encoding. However
- PaperSliMOO: Interpretable Multi-Objective Evolutionary Search for LLM Depth Pruning
As large language models (LLMs) grow more powerful, their scale makes them costly to serve. Depth pruning offers a practical route to efficiency by directly reducing memo
- PaperCertified Adaptive List Decoding for Error-Correcting Codes
Maximum-likelihood (ML) decoding of linear block codes is NP-hard, so neural decoders are evaluated by how closely they approach it. We observe that proving a candidate o
Headlines and links come from the labs' own blogs, Hacker News and the GDELT news index, and from the sources our index already follows. We show a short excerpt and always link to the original. A launch reported by a news site rather than the lab is marked “Reported” until the lab itself announces it.