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...
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...
MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
arXiv:2602.17868v1 Announce Type: cross Abstract: Developing foundation models for time series classification is of high practical relevance, as such models can serve as universal feature extractors for diverse downstream tasks. Although early models such as Mantis have shown the promise...
Games That Teach, Chats That Convince: Comparing Interactive and Static Formats for Persuasive Learning
arXiv:2602.17905v1 Announce Type: cross Abstract: Interactive systems such as chatbots and games are increasingly used to persuade and educate on sustainability-related topics, yet it remains unclear how different delivery formats shape learning and persuasive outcomes when content is held constant....
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...
MIRA: Memory-Integrated Reinforcement Learning Agent with Limited LLM Guidance
arXiv:2602.17930v1 Announce Type: cross Abstract: Reinforcement learning (RL) agents often suffer from high sample complexity in sparse or delayed reward settings due to limited prior structure. Large language models (LLMs) can provide subgoal decompositions, plausible trajectories, and abstract priors that...
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...
On the scaling relationship between cloze probabilities and language model next-token prediction
arXiv:2602.17848v1 Announce Type: new Abstract: Recent work has shown that larger language models have better predictive power for eye movement and reading time data. While even the best models under-allocate probability mass to human responses, larger models assign higher-quality estimates...
Analyzing LLM Instruction Optimization for Tabular Fact Verification
arXiv:2602.17937v1 Announce Type: new Abstract: Instruction optimization provides a lightweight, model-agnostic approach to enhancing the reasoning performance of large language models (LLMs). This paper presents the first systematic comparison of instruction optimization, based on the DSPy optimization framework, for tabular...
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...
Perceived Political Bias in LLMs Reduces Persuasive Abilities
arXiv:2602.18092v1 Announce Type: new Abstract: Conversational AI has been proposed as a scalable way to correct public misconceptions and spread misinformation. Yet its effectiveness may depend on perceptions of its political neutrality. As LLMs enter partisan conflict, elites increasingly portray...
Agentic Adversarial QA for Improving Domain-Specific LLMs
arXiv:2602.18137v1 Announce Type: new Abstract: Large Language Models (LLMs), despite extensive pretraining on broad internet corpora, often struggle to adapt effectively to specialized domains. There is growing interest in fine-tuning these models for such domains; however, progress is constrained by...
Information-Theoretic Storage Cost in Sentence Comprehension
arXiv:2602.18217v1 Announce Type: new Abstract: Real-time sentence comprehension imposes a significant load on working memory, as comprehenders must maintain contextual information to anticipate future input. While measures of such load have played an important role in psycholinguistic theories, they have...
Thinking by Subtraction: Confidence-Driven Contrastive Decoding for LLM Reasoning
arXiv:2602.18232v1 Announce Type: new Abstract: Recent work on test-time scaling for large language model (LLM) reasoning typically assumes that allocating more inference-time computation uniformly improves correctness. However, prior studies show that reasoning uncertainty is highly localized: a small subset of...
PsihoRo: Depression and Anxiety Romanian Text Corpus
arXiv:2602.18324v1 Announce Type: new Abstract: Psychological corpora in NLP are collections of texts used to analyze human psychology, emotions, and mental health. These texts allow researchers to study psychological constructs, detect mental health issues and analyze emotional language. However, mental...
SPQ: An Ensemble Technique for Large Language Model Compression
arXiv:2602.18420v1 Announce Type: new Abstract: This study presents an ensemble technique, SPQ (SVD-Pruning-Quantization), for large language model (LLM) compression that combines variance-retained singular value decomposition (SVD), activation-based pruning, and post-training linear quantization. Each component targets a different source of inefficiency:...
Lost Before Translation: Social Information Transmission and Survival in AI-AI Communication
arXiv:2602.17674v1 Announce Type: cross Abstract: When AI systems summarize and relay information, they inevitably transform it. But how? We introduce an experimental paradigm based on the telephone game to study what happens when AI talks to AI. Across five studies...
Tethered Reasoning: Decoupling Entropy from Hallucination in Quantized LLMs via Manifold Steering
arXiv:2602.17691v1 Announce Type: cross Abstract: Quantized language models face a fundamental dilemma: low sampling temperatures yield repetitive, mode-collapsed outputs, while high temperatures (T > 2.0) cause trajectory divergence and semantic incoherence. We present HELIX, a geometric framework that decouples output...
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...
TFL: Targeted Bit-Flip Attack on Large Language Model
arXiv:2602.17837v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks. Recent studies have shown that bit-flip attacks (BFAs), which exploit computer...
NIMMGen: Learning Neural-Integrated Mechanistic Digital Twins with LLMs
arXiv:2602.18008v1 Announce Type: cross Abstract: Mechanistic models encode scientific knowledge about dynamical systems and are widely used in downstream scientific and policy applications. Recent work has explored LLM-based agentic frameworks to automatically construct mechanistic models from data; however, existing problem...
Analyzing and Improving Chain-of-Thought Monitorability Through Information Theory
arXiv:2602.18297v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitors are LLM-based systems that analyze reasoning traces to detect when outputs may exhibit attributes of interest, such as test-hacking behavior during code generation. In this paper, we use information-theoretic analysis to show...
On the Semantic and Syntactic Information Encoded in Proto-Tokens for One-Step Text Reconstruction
arXiv:2602.18301v1 Announce Type: cross Abstract: Autoregressive large language models (LLMs) generate text token-by-token, requiring n forward passes to produce a sequence of length n. Recent work, Exploring the Latent Capacity of LLMs for One-Step Text Reconstruction (Mezentsev and Oseledets), shows...
Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization
arXiv:2602.17679v1 Announce Type: new Abstract: Bayesian optimization (BO) is a powerful method for optimizing black-box manufacturing processes, but its performance is often limited when dealing with high-dimensional multi-stage systems, where we can observe intermediate outputs. Standard BO models the process...
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...
Optimal Multi-Debris Mission Planning in LEO: A Deep Reinforcement Learning Approach with Co-Elliptic Transfers and Refueling
arXiv:2602.17685v1 Announce Type: new Abstract: This paper addresses the challenge of multi target active debris removal (ADR) in Low Earth Orbit (LEO) by introducing a unified coelliptic maneuver framework that combines Hohmann transfers, safety ellipse proximity operations, and explicit refueling...
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...
Asking Forever: Universal Activations Behind Turn Amplification in Conversational LLMs
arXiv:2602.17778v1 Announce Type: new Abstract: Multi-turn interaction length is a dominant factor in the operational costs of conversational LLMs. In this work, we present a new failure mode in conversational LLMs: turn amplification, in which a model consistently prolongs multi-turn...