arXiv:2604.00181v1 Announce Type: cross Abstract: This paper brings up this idea of using Near Field Communication (NFC) for inventory control system instead of using traditional barcodes. NFC because of its high security, ease of use and efficiency can be very suitable for systems like inventory control. In traditional inventory control systems, each product has a […]
When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Attribution
arXiv:2603.17445v4 Announce Type: replace Abstract: When a multi-agent system produces an incorrect or harmful answer, who is accountable if execution logs and agent identifiers are unavailable? In practice, generated content is often detached from its execution environment due to privacy or system boundaries, leaving the final text as the only auditable artifact. Existing attribution methods […]
Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation
arXiv:2604.00536v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong downstream performance largely due to abundant supervised fine-tuning (SFT) data. However, high-quality SFT data in knowledge-intensive domains such as humanities, social sciences, medicine, law, and finance is scarce because expert curation is expensive, privacy constraints are strict, and label consistency is hard to ensure. […]
Temporal Dependencies in In-Context Learning: The Role of Induction Heads
arXiv:2604.01094v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong in-context learning capabilities, but how they track and retrieve information from context remains underexplored. Drawing on the free recall paradigm in cognitive science (where participants recall list items in any order), we show that several open-source LLMs consistently display a serial-recall-like pattern, assigning peak […]
Structured Prompts Improve Evaluation of Language Models
arXiv:2511.20836v3 Announce Type: replace-cross Abstract: As language models (LMs) are increasingly adopted across domains, high-quality benchmarking frameworks are essential for guiding deployment decisions. In practice, however, frameworks such as Holistic Evaluation of Language Models (HELM) typically evaluate models under a single static prompt configuration, even though model behavior depends strongly on prompt choice. As a […]
PReD: An LLM-based Foundation Multimodal Model for Electromagnetic Perception, Recognition, and Decision
arXiv:2603.28183v2 Announce Type: replace Abstract: Multimodal Large Language Models have demonstrated powerful cross-modal understanding and reasoning capabilities in general domains. However, in the electromagnetic (EM) domain, they still face challenges such as data scarcity and insufficient integration of domain knowledge. This paper proposes PReD, the first foundation model for the EM domain that covers the […]
WARP: Guaranteed Inner-Layer Repair of NLP Transformers
arXiv:2604.00938v1 Announce Type: cross Abstract: Transformer-based NLP models remain vulnerable to adversarial perturbations, yet existing repair methods face a fundamental trade-off: gradient-based approaches offer flexibility but lack verifiability and often overfit; methods that do provide repair guarantees are restricted to the final layer or small networks, significantly limiting the parameter search space available for repair. […]
IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models
arXiv:2604.00757v1 Announce Type: cross Abstract: Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens. Existing token pruning methods mitigate this issue through empirical approaches while overlooking the internal mechanism of attention. In this paper, we propose a novel training […]
Non-ignorable fuzziness in granular counts: the case of RNA-seq data
arXiv:2604.00763v1 Announce Type: cross Abstract: RNA-seq count data are often affected by read-to-gene alignment ambiguity, especially in high-dimensional transcriptomics. This type of ambiguity can be conveniently expressed through granular counts, namely fuzzy-valued observations of latent discrete quantities. We study a class of fuzzy-reporting mechanisms and show that, when reporting exploits graded membership, ignorability fails generically, […]
From Density Matrices to Phase Transitions in Deep Learning: Spectral Early Warnings and Interpretability
arXiv:2603.29805v2 Announce Type: replace-cross Abstract: A key problem in the modern study of AI is predicting and understanding emergent capabilities in models during training. Inspired by methods for studying reactions in quantum chemistry, we present the “2-datapoint reduced density matrix”. We show that this object provides a computationally efficient, unified observable of phase transitions during […]
How Emotion Shapes the Behavior of LLMs and Agents: A Mechanistic Study
arXiv:2604.00005v1 Announce Type: new Abstract: Emotion plays an important role in human cognition and performance. Motivated by this, we investigate whether analogous emotional signals can shape the behavior of large language models (LLMs) and agents. Existing emotion-aware studies mainly treat emotion as a surface-level style factor or a perception target, overlooking its mechanistic role in […]
$textttYC-Bench$: Benchmarking AI Agents for Long-Term Planning and Consistent Execution
arXiv:2604.01212v1 Announce Type: cross Abstract: As LLM agents tackle increasingly complex tasks, a critical question is whether they can maintain strategic coherence over long horizons: planning under uncertainty, learning from delayed feedback, and adapting when early mistakes compound. We introduce $textttYC-Bench$, a benchmark that evaluates these capabilities by tasking an agent with running a simulated […]