Understanding the Interplay between LLMs' Utilisation of Parametric and Contextual Knowledge: A keynote at ECIR 2025
arXiv:2603.09654v1 Announce Type: new Abstract: Language Models (LMs) acquire parametric knowledge from their training process, embedding it within their weights. The increasing scalability of LMs, however, poses significant challenges for understanding a model's inner workings and further for updating or...
Beyond Fine-Tuning: Robust Food Entity Linking under Ontology Drift with FoodOntoRAG
arXiv:2603.09758v1 Announce Type: new Abstract: Standardizing food terms from product labels and menus into ontology concepts is a prerequisite for trustworthy dietary assessment and safety reporting. The dominant approach to Named Entity Linking (NEL) in the food and nutrition domains...
Chow-Liu Ordering for Long-Context Reasoning in Chain-of-Agents
arXiv:2603.09835v1 Announce Type: new Abstract: Sequential multi-agent reasoning frameworks such as Chain-of-Agents (CoA) handle long-context queries by decomposing inputs into chunks and processing them sequentially using LLM-based worker agents that read from and update a bounded shared memory. From a...
VoxEmo: Benchmarking Speech Emotion Recognition with Speech LLMs
arXiv:2603.08936v1 Announce Type: cross Abstract: Speech Large Language Models (LLMs) show great promise for speech emotion recognition (SER) via generative interfaces. However, shifting from closed-set classification to open text generation introduces zero-shot stochasticity, making evaluation highly sensitive to prompts. Additionally,...
BiCLIP: Domain Canonicalization via Structured Geometric Transformation
arXiv:2603.08942v1 Announce Type: cross Abstract: Recent advances in vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities, yet adapting these models to specialized domains remains a significant challenge. Building on recent theoretical insights suggesting that independently trained VLMs are related by...
Multi-level meta-reinforcement learning with skill-based curriculum
arXiv:2603.08773v1 Announce Type: new Abstract: We consider problems in sequential decision making with natural multi-level structure, where sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure has remained a longstanding challenge; we describe an efficient...
Expressivity-Efficiency Tradeoffs for Hybrid Sequence Models
arXiv:2603.08859v1 Announce Type: new Abstract: Hybrid sequence models--combining Transformer and state-space model layers--seek to gain the expressive versatility of attention as well as the computational efficiency of state-space model layers. Despite burgeoning interest in hybrid models, we lack a basic...
Uncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gates
arXiv:2603.08914v1 Announce Type: new Abstract: Over-parameterized neural networks incur prohibitive memory and computational costs for resource-constrained deployment. The Strong Lottery Ticket (SLT) hypothesis suggests that randomly initialized networks contain sparse subnetworks achieving competitive accuracy without weight training. Existing SLT methods,...
Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation
arXiv:2603.09053v1 Announce Type: new Abstract: Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment, and has become essential in mission-critical domains such as supply chains and industrial systems. However, simulators learned from noisy or biased real-world...
Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL
arXiv:2603.09161v1 Announce Type: new Abstract: Learning effective netlist representations is fundamentally constrained by the scarcity of labeled datasets, as real designs are protected by Intellectual Property (IP) and costly to annotate. Existing work therefore focuses on small-scale circuits with clean...
Proxy-Guided Measurement Calibration
arXiv:2603.09288v1 Announce Type: new Abstract: Aggregate outcome variables collected through surveys and administrative records are often subject to systematic measurement error. For instance, in disaster loss databases, county-level losses reported may differ from the true damages due to variations in...
The how and why of gun control
A Second Opinion is a recurring series by Haley Proctor on the Second Amendment and constitutional litigation. Last Monday, the Supreme Court heard argument in United States v. Hemani. In […]The postThe how and why of gun controlappeared first onSCOTUSblog.
AI Now Co-ED Amba Kak Gives Remarks Before the UN General Assembly on AI Governance - AI Now Institute
Google gives in to users’ complaints over AI-powered ‘Ask Photos’ search feature
The option appears on the Google Photos Search screen and lets users pick which experience they want.
Dissecting racial bias in an algorithm used to manage the health of populations
Racial bias in health algorithms The U.S. health care system uses commercial algorithms to guide health decisions. Obermeyer et al. find evidence of racial bias in one widely used algorithm, such that Black patients assigned the same level of risk...
A Dynamic Self-Evolving Extraction System
arXiv:2603.06915v1 Announce Type: new Abstract: The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation. High-quality extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and...
Hierarchical Latent Structures in Data Generation Process Unify Mechanistic Phenomena across Scale
arXiv:2603.06592v1 Announce Type: new Abstract: Contemporary studies have uncovered many puzzling phenomena in the neural information processing of Transformer-based language models. Building a robust, unified understanding of these phenomena requires disassembling a model within the scope of its training. While...
"Dark Triad" Model Organisms of Misalignment: Narrow Fine-Tuning Mirrors Human Antisocial Behavior
arXiv:2603.06816v1 Announce Type: new Abstract: The alignment problem refers to concerns regarding powerful intelligences, ensuring compatibility with human preferences and values as capabilities increase. Current large language models (LLMs) show misaligned behaviors, such as strategic deception, manipulation, and reward-seeking, that...
Hit-RAG: Learning to Reason with Long Contexts via Preference Alignment
arXiv:2603.07023v1 Announce Type: new Abstract: Despite the promise of Retrieval-Augmented Generation in grounding Multimodal Large Language Models with external knowledge, the transition to extensive contexts often leads to significant attention dilution and reasoning hallucinations. The surge in information density causes...
Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster
arXiv:2603.07238v1 Announce Type: new Abstract: Similarities between language representations derived from Self-Supervised Speech Models (S3Ms) have been observed to primarily reflect geographic proximity or surface typological similarities driven by recent expansion or contact, potentially missing deeper genealogical signals. We investigate...
Cross-Modal Taxonomic Generalization in (Vision-) Language Models
arXiv:2603.07474v1 Announce Type: new Abstract: What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grounded evidence? We study this question for a scenario where part of the input...
TableMind++: An Uncertainty-Aware Programmatic Agent for Tool-Augmented Table Reasoning
arXiv:2603.07528v1 Announce Type: new Abstract: Table reasoning requires models to jointly perform semantic understanding and precise numerical operations. Most existing methods rely on a single-turn reasoning paradigm over tables which suffers from context overflow and weak numerical sensitivity. To address...
StyleBench: Evaluating Speech Language Models on Conversational Speaking Style Control
arXiv:2603.07599v1 Announce Type: new Abstract: Speech language models (SLMs) have significantly extended the interactive capability of text-based Large Language Models (LLMs) by incorporating paralinguistic information. For more realistic interactive experience with customized styles, current SLMs have managed to interpret and...
Whitening Reveals Cluster Commitment as the Geometric Separator of Hallucination Types
arXiv:2603.07755v1 Announce Type: new Abstract: A geometric hallucination taxonomy distinguishes three failure types -- center-drift (Type~1), wrong-well convergence (Type~2), and coverage gaps (Type~3) -- by their signatures in embedding cluster space. Prior work found Types~1 and~2 indistinguishable in full-dimensional contextual...
Dual-Metric Evaluation of Social Bias in Large Language Models: Evidence from an Underrepresented Nepali Cultural Context
arXiv:2603.07792v1 Announce Type: new Abstract: Large language models (LLMs) increasingly influence global digital ecosystems, yet their potential to perpetuate social and cultural biases remains poorly understood in underrepresented contexts. This study presents a systematic analysis of representational biases in seven...
Scale Dependent Data Duplication
arXiv:2603.06603v1 Announce Type: new Abstract: Data duplication during pretraining can degrade generalization and lead to memorization, motivating aggressive deduplication pipelines. However, at web scale, it is unclear what constitutes a ``duplicate'': beyond surface-form matches, semantically equivalent documents (e.g. translations) may...
Evo: Autoregressive-Diffusion Large Language Models with Evolving Balance
arXiv:2603.06617v1 Announce Type: new Abstract: We introduce \textbf{Evo}, a duality latent trajectory model that bridges autoregressive (AR) and diffusion-based language generation within a continuous evolutionary generative framework. Rather than treating AR decoding and diffusion generation as separate paradigms, Evo reconceptualizes...
Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks
arXiv:2603.06618v1 Announce Type: new Abstract: Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties...
Graph Property Inference in Small Language Models: Effects of Representation and Inference Strategy
arXiv:2603.06635v1 Announce Type: new Abstract: Recent progress in language modeling has expanded the range of tasks that can be approached through natural language interfaces, including problems that require structured reasoning. However, it remains unclear how effectively limited-capacity language models can...
From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories
arXiv:2603.06720v1 Announce Type: new Abstract: Access to electronic health records (EHRs) for digital health research is often limited by privacy regulations and institutional barriers. Synthetic EHRs have been proposed as a way to enable safe and sovereign data sharing; however,...