The Generation-Recognition Asymmetry: Six Dimensions of a Fundamental Divide in Formal Language Theory
arXiv:2603.10139v1 Announce Type: new Abstract: Every formal grammar defines a language and can in principle be used in three ways: to generate strings (production), to recognize them (parsing), or -- given only examples -- to infer the grammar itself (grammar...
Reason and Verify: A Framework for Faithful Retrieval-Augmented Generation
arXiv:2603.10143v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) significantly improves the factuality of Large Language Models (LLMs), yet standard pipelines often lack mechanisms to verify inter- mediate reasoning, leaving them vulnerable to hallucinations in high-stakes domains. To address this, we...
Lost in Backpropagation: The LM Head is a Gradient Bottleneck
arXiv:2603.10145v1 Announce Type: new Abstract: The last layer of neural language models (LMs) projects output features of dimension $D$ to logits in dimension $V$, the size of the vocabulary, where usually $D \ll V$. This mismatch is known to raise...
OpenClaw-RL: Train Any Agent Simply by Talking
arXiv:2603.10165v1 Announce Type: new Abstract: Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system recovers it as a live, online learning...
Adaptive Activation Cancellation for Hallucination Mitigation in Large Language Models
arXiv:2603.10195v1 Announce Type: new Abstract: Large Language Models frequently generate fluent but factually incorrect text. We propose Adaptive Activation Cancellation (AAC), a real-time inference-time framework that treats hallucination-associated neural activations as structured interference within the transformer residual stream, drawing an...
GR-SAP: Generative Replay for Safety Alignment Preservation during Fine-Tuning
arXiv:2603.10243v1 Announce Type: new Abstract: Recent studies show that the safety alignment of large language models (LLMs) can be easily compromised even by seemingly non-adversarial fine-tuning. To preserve safety alignment during fine-tuning, a widely used strategy is to jointly optimize...
Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas
arXiv:2603.10303v1 Announce Type: new Abstract: Judging the novelty of research ideas is crucial for advancing science, enabling the identification of unexplored directions, and ensuring contributions meaningfully extend existing knowledge rather than reiterate minor variations. However, given the exponential growth of...
Mitigating Translationese Bias in Multilingual LLM-as-a-Judge via Disentangled Information Bottleneck
arXiv:2603.10351v1 Announce Type: new Abstract: Large language models (LLMs) have become a standard for multilingual evaluation, yet they exhibit a severe systematic translationese bias. In this paper, translationese bias is characterized as LLMs systematically favoring machine-translated text over human-authored references,...
Dynamic Knowledge Fusion for Multi-Domain Dialogue State Tracking
arXiv:2603.10367v1 Announce Type: new Abstract: The performance of task-oriented dialogue models is strongly tied to how well they track dialogue states, which records and updates user information across multi-turn interactions. However, current multi-domain DST encounters two key challenges: the difficulty...
Aligning Large Language Models with Searcher Preferences
arXiv:2603.10473v1 Announce Type: new Abstract: The paradigm shift from item-centric ranking to answer-centric synthesis is redefining the role of search engines. While recent industrial progress has applied generative techniques to closed-set item ranking in e-commerce, research and deployment of open-ended...
Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs
arXiv:2603.10476v1 Announce Type: new Abstract: The alignment of large language models (LLMs) has progressed substantially in single-agent settings through paradigms such as RLHF and Constitutional AI, with recent work exploring scalable alternatives such as RLAIF and evolving alignment objectives. However,...
LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learning
arXiv:2603.10024v1 Announce Type: new Abstract: LWM-Temporal is a new member of the Large Wireless Models (LWM) family that targets the spatiotemporal nature of wireless channels. Designed as a task-agnostic foundation model, LWM-Temporal learns universal channel embeddings that capture mobility-induced evolution...
Gated Adaptation for Continual Learning in Human Activity Recognition
arXiv:2603.10046v1 Announce Type: new Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR). Continual learning systems...
Revisiting Sharpness-Aware Minimization: A More Faithful and Effective Implementation
arXiv:2603.10048v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) enhances generalization by minimizing the maximum training loss within a predefined neighborhood around the parameters. However, its practical implementation approximates this as gradient ascent(s) followed by applying the gradient at the ascent...
InFusionLayer: a CFA-based ensemble tool to generate new classifiers for learning and modeling
arXiv:2603.10049v1 Announce Type: new Abstract: Ensemble learning is a well established body of methods for machine learning to enhance predictive performance by combining multiple algorithms/models. Combinatorial Fusion Analysis (CFA) has provided method and practice for combining multiple scoring systems, using...
Cluster-Aware Attention-Based Deep Reinforcement Learning for Pickup and Delivery Problems
arXiv:2603.10053v1 Announce Type: new Abstract: The Pickup and Delivery Problem (PDP) is a fundamental and challenging variant of the Vehicle Routing Problem, characterized by tightly coupled pickup--delivery pairs, precedence constraints, and spatial layouts that often exhibit clustering. Existing deep reinforcement...
Improving Search Agent with One Line of Code
arXiv:2603.10069v1 Announce Type: new Abstract: Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools for a multi-turn information-seeking process autonomously. However, we identify a critical training instability that leads...
Dissecting Chronos: Sparse Autoencoders Reveal Causal Feature Hierarchies in Time Series Foundation Models
arXiv:2603.10071v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are increasingly deployed in high-stakes domains, yet their internal representations remain opaque. We present the first application of sparse autoencoders (SAEs) to a TSFM, training TopK SAEs on activations of...
Marginals Before Conditionals
arXiv:2603.10074v1 Announce Type: new Abstract: We construct a minimal task that isolates conditional learning in neural networks: a surjective map with K-fold ambiguity, resolved by a selector token z, so H(A | B) = log K while H(A | B,...
Large Spikes in Stochastic Gradient Descent: A Large-Deviations View
arXiv:2603.10079v1 Announce Type: new Abstract: We analyse SGD training of a shallow, fully connected network in the NTK scaling and provide a quantitative theory of the catapult phase. We identify an explicit criterion separating two behaviours: When an explicit function...
Digging Deeper: Learning Multi-Level Concept Hierarchies
arXiv:2603.10084v1 Announce Type: new Abstract: Although concept-based models promise interpretability by explaining predictions with human-understandable concepts, they typically rely on exhaustive annotations and treat concepts as flat and independent. To circumvent this, recent work has introduced Hierarchical Concept Embedding Models...
KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization
arXiv:2603.10085v1 Announce Type: new Abstract: Improving GPU kernel efficiency is crucial for advancing AI systems. Recent work has explored leveraging large language models (LLMs) for GPU kernel generation and optimization. However, existing LLM-based kernel optimization pipelines typically rely on opaque,...
ES-dLLM: Efficient Inference for Diffusion Large Language Models by Early-Skipping
arXiv:2603.10088v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) are emerging as a promising alternative to autoregressive models (ARMs) due to their ability to capture bidirectional context and the potential for parallel generation. Despite the advantages, dLLM inference remains...
A Survey of Weight Space Learning: Understanding, Representation, and Generation
arXiv:2603.10090v1 Announce Type: new Abstract: Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances show that the set of all possible weight values...
Rethinking Adam for Time Series Forecasting: A Simple Heuristic to Improve Optimization under Distribution Shifts
arXiv:2603.10095v1 Announce Type: new Abstract: Time-series forecasting often faces challenges from non-stationarity, particularly distributional drift, where the data distribution evolves over time. This dynamic behavior can undermine the effectiveness of adaptive optimizers, such as Adam, which are typically designed for...
Denoising the US Census: Succinct Block Hierarchical Regression
arXiv:2603.10099v1 Announce Type: new Abstract: The US Census Bureau Disclosure Avoidance System (DAS) balances confidentiality and utility requirements for the decennial US Census (Abowd et al., 2022). The DAS was used in the 2020 Census to produce demographic datasets critically...
Hardware Efficient Approximate Convolution with Tunable Error Tolerance for CNNs
arXiv:2603.10100v1 Announce Type: new Abstract: Modern CNNs' high computational demands hinder edge deployment, as traditional ``hard'' sparsity (skipping mathematical zeros) loses effectiveness in deep layers or with smooth activations like Tanh. We propose a ``soft sparsity'' paradigm using a hardware...
CLIPO: Contrastive Learning in Policy Optimization Generalizes RLVR
arXiv:2603.10101v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capacity of Large Language Models (LLMs). However, RLVR solely relies on final answers as outcome rewards, neglecting the correctness of intermediate reasoning steps. Training...
Lost in the Middle at Birth: An Exact Theory of Transformer Position Bias
arXiv:2603.10123v1 Announce Type: new Abstract: The ``Lost in the Middle'' phenomenon -- a U-shaped performance curve where LLMs retrieve well from the beginning and end of a context but fail in the middle -- is widely attributed to learned Softmax...
A neural operator for predicting vibration frequency response curves from limited data
arXiv:2603.10149v1 Announce Type: new Abstract: In the design of engineered components, rigorous vibration testing is essential for performance validation and identification of resonant frequencies and amplitudes encountered during operation. Performing this evaluation numerically via machine learning has great potential to...