QueryPlot: Generating Geological Evidence Layers using Natural Language Queries for Mineral Exploration
arXiv:2602.17784v1 Announce Type: cross Abstract: Mineral prospectivity mapping requires synthesizing heterogeneous geological knowledge, including textual deposit models and geospatial datasets, to identify regions likely to host specific mineral deposit types. This process is traditionally manual and knowledge-intensive. We present QueryPlot,...
Mind the Style: Impact of Communication Style on Human-Chatbot Interaction
arXiv:2602.17850v1 Announce Type: cross Abstract: Conversational agents increasingly mediate everyday digital interactions, yet the effects of their communication style on user experience and task success remain unclear. Addressing this gap, we describe the results of a between-subject user study where...
Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems
arXiv:2602.17856v1 Announce Type: cross Abstract: This paper investigates the enhancement of scientific literature chatbots through retrieval-augmented generation (RAG), with a focus on evaluating vector- and graph-based retrieval systems. The proposed chatbot leverages both structured (graph) and unstructured (vector) databases to...
Financial time series augmentation using transformer based GAN architecture
arXiv:2602.17865v1 Announce Type: cross Abstract: Time-series forecasting is a critical task across many domains, from engineering to economics, where accurate predictions drive strategic decisions. However, applying advanced deep learning models in challenging, volatile domains like finance is difficult due to...
Understanding the Fine-Grained Knowledge Capabilities of Vision-Language Models
arXiv:2602.17871v1 Announce Type: cross Abstract: Vision-language models (VLMs) have made substantial progress across a wide range of visual question answering benchmarks, spanning visual reasoning, document understanding, and multimodal dialogue. These improvements are evident in a wide range of VLMs built...
Understanding Unreliability of Steering Vectors in Language Models: Geometric Predictors and the Limits of Linear Approximations
arXiv:2602.17881v1 Announce Type: cross Abstract: Steering vectors are a lightweight method for controlling language model behavior by adding a learned bias to the activations at inference time. Although effective on average, steering effect sizes vary across samples and are unreliable...
From Lossy to Verified: A Provenance-Aware Tiered Memory for Agents
arXiv:2602.17913v1 Announce Type: cross Abstract: Long-horizon agents often compress interaction histories into write-time summaries. This creates a fundamental write-before-query barrier: compression decisions are made before the system knows what a future query will hinge on. As a result, summaries can...
Neural Synchrony Between Socially Interacting Language Models
arXiv:2602.17815v1 Announce Type: new Abstract: Neuroscience has uncovered a fundamental mechanism of our social nature: human brain activity becomes synchronized with others in many social contexts involving interaction. Traditionally, social minds have been regarded as an exclusive property of living...
Decomposing Retrieval Failures in RAG for Long-Document Financial Question Answering
arXiv:2602.17981v1 Announce Type: new Abstract: Retrieval-augmented generation is increasingly used for financial question answering over long regulatory filings, yet reliability depends on retrieving the exact context needed to justify answers in high stakes settings. We study a frequent failure mode...
Vichara: Appellate Judgment Prediction and Explanation for the Indian Judicial System
arXiv:2602.18346v1 Announce Type: new Abstract: In jurisdictions like India, where courts face an extensive backlog of cases, artificial intelligence offers transformative potential for legal judgment prediction. A critical subset of this backlog comprises appellate cases, which are formal decisions issued...
Validating Political Position Predictions of Arguments
arXiv:2602.18351v1 Announce Type: new Abstract: Real-world knowledge representation often requires capturing subjective, continuous attributes -- such as political positions -- that conflict with pairwise validation, the widely accepted gold standard for human evaluation. We address this challenge through a dual-scale...
Reducing Text Bias in Synthetically Generated MCQAs for VLMs in Autonomous Driving
arXiv:2602.17677v1 Announce Type: cross Abstract: Multiple Choice Question Answering (MCQA) benchmarks are an established standard for measuring Vision Language Model (VLM) performance in driving tasks. However, we observe the known phenomenon that synthetically generated MCQAs are highly susceptible to hidden...
Bayesian Optimality of In-Context Learning with Selective State Spaces
arXiv:2602.17744v1 Announce Type: cross Abstract: We propose Bayesian optimal sequential prediction as a new principle for understanding in-context learning (ICL). Unlike interpretations framing Transformers as performing implicit gradient descent, we formalize ICL as meta-learning over latent sequence tasks. For tasks...
ADAPT: Hybrid Prompt Optimization for LLM Feature Visualization
arXiv:2602.17867v1 Announce Type: cross Abstract: Understanding what features are encoded by learned directions in LLM activation space requires identifying inputs that strongly activate them. Feature visualization, which optimizes inputs to maximally activate a target direction, offers an alternative to costly...
BioBridge: Bridging Proteins and Language for Enhanced Biological Reasoning with LLMs
arXiv:2602.17680v1 Announce Type: new Abstract: Existing Protein Language Models (PLMs) often suffer from limited adaptability to multiple tasks and exhibit poor generalization across diverse biological contexts. In contrast, general-purpose Large Language Models (LLMs) lack the capability to interpret protein sequences...
Parallel Complex Diffusion for Scalable Time Series Generation
arXiv:2602.17706v1 Announce Type: new Abstract: Modeling long-range dependencies in time series generation poses a fundamental trade-off between representational capacity and computational efficiency. Traditional temporal diffusion models suffer from local entanglement and the $\mathcal{O}(L^2)$ cost of attention mechanisms. We address these...
Provable Adversarial Robustness in In-Context Learning
arXiv:2602.17743v1 Announce Type: new Abstract: Large language models adapt to new tasks through in-context learning (ICL) without parameter updates. Current theoretical explanations for this capability assume test tasks are drawn from a distribution similar to that seen during pretraining. This...
Avoid What You Know: Divergent Trajectory Balance for GFlowNets
arXiv:2602.17827v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) are a flexible family of amortized samplers trained to generate discrete and compositional objects with probability proportional to a reward function. However, learning efficiency is constrained by the model's ability to...
Two Calm Ends and the Wild Middle: A Geometric Picture of Memorization in Diffusion Models
arXiv:2602.17846v1 Announce Type: new Abstract: Diffusion models generate high-quality samples but can also memorize training data, raising serious privacy concerns. Understanding the mechanisms governing when memorization versus generalization occurs remains an active area of research. In particular, it is unclear...
Memory-Based Advantage Shaping for LLM-Guided Reinforcement Learning
arXiv:2602.17931v1 Announce Type: new Abstract: In environments with sparse or delayed rewards, reinforcement learning (RL) incurs high sample complexity due to the large number of interactions needed for learning. This limitation has motivated the use of large language models (LLMs)...
Understanding the Generalization of Bilevel Programming in Hyperparameter Optimization: A Tale of Bias-Variance Decomposition
arXiv:2602.17947v1 Announce Type: new Abstract: Gradient-based hyperparameter optimization (HPO) have emerged recently, leveraging bilevel programming techniques to optimize hyperparameter by estimating hypergradient w.r.t. validation loss. Nevertheless, previous theoretical works mainly focus on reducing the gap between the estimation and ground-truth...
With AI, investor loyalty is (almost) dead: At least a dozen OpenAI VCs now also back Anthropic
While some dual investors are understandable, others were more shocking, and signal the disregard of a longstanding ethical conflict-of-interest rule.
How Vision Becomes Language: A Layer-wise Information-Theoretic Analysis of Multimodal Reasoning
arXiv:2602.15580v1 Announce Type: new Abstract: When a multimodal Transformer answers a visual question, is the prediction driven by visual evidence, linguistic reasoning, or genuinely fused cross-modal computation -- and how does this structure evolve across layers? We address this question...
CARE Drive A Framework for Evaluating Reason-Responsiveness of Vision Language Models in Automated Driving
arXiv:2602.15645v1 Announce Type: new Abstract: Foundation models, including vision language models, are increasingly used in automated driving to interpret scenes, recommend actions, and generate natural language explanations. However, existing evaluation methods primarily assess outcome based performance, such as safety and...
PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra
arXiv:2602.15669v1 Announce Type: new Abstract: Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning, failing to capture the dynamic and compositional nature of human traits. We introduce PERSONA, a training-free framework that achieves...
This human study did not involve human subjects: Validating LLM simulations as behavioral evidence
arXiv:2602.15785v1 Announce Type: new Abstract: A growing literature uses large language models (LLMs) as synthetic participants to generate cost-effective and nearly instantaneous responses in social science experiments. However, there is limited guidance on when such simulations support valid inference about...
CLOT: Closed-Loop Global Motion Tracking for Whole-Body Humanoid Teleoperation
arXiv:2602.15060v1 Announce Type: cross Abstract: Long-horizon whole-body humanoid teleoperation remains challenging due to accumulated global pose drift, particularly on full-sized humanoids. Although recent learning-based tracking methods enable agile and coordinated motions, they typically operate in the robot's local frame and...
Safe-SDL:Establishing Safety Boundaries and Control Mechanisms for AI-Driven Self-Driving Laboratories
arXiv:2602.15061v1 Announce Type: cross Abstract: The emergence of Self-Driving Laboratories (SDLs) transforms scientific discovery methodology by integrating AI with robotic automation to create closed-loop experimental systems capable of autonomous hypothesis generation, experimentation, and analysis. While promising to compress research timelines...
Exploiting Layer-Specific Vulnerabilities to Backdoor Attack in Federated Learning
arXiv:2602.15161v1 Announce Type: cross Abstract: Federated learning (FL) enables distributed model training across edge devices while preserving data locality. This decentralized approach has emerged as a promising solution for collaborative learning on sensitive user data, effectively addressing the longstanding privacy...
Extracting Consumer Insight from Text: A Large Language Model Approach to Emotion and Evaluation Measurement
arXiv:2602.15312v1 Announce Type: new Abstract: Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice. This study introduces the Linguistic eXtractor (LX), a fine-tuned, large language model trained on consumer-authored text that...