arXiv:2604.18621v1 Announce Type: new Abstract: Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In this study, we apply QML to identify subtype specific biomarkers for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two predominant forms […]
VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
arXiv:2604.19728v1 Announce Type: cross Abstract: We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize on the action training stage, often stitching together incompatible pretraining pipelines. VLA Foundry instead provides a shared training stack with end-to-end control, from language pretraining to action-expert […]
Plug-and-Play Dramaturge: A Divide-and-Conquer Approach for Iterative Narrative Script Refinement via Collaborative LLM Agents
arXiv:2510.05188v4 Announce Type: replace Abstract: Although LLMs have been widely adopted for creative content generation, a single-pass process often struggles to produce high-quality long narratives. How to effectively revise and improve long narrative scripts like scriptwriters remains a significant challenge, as it demands a comprehensive understanding of the entire context to identify global structural issues […]
MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization
arXiv:2604.06798v4 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) based large language models (LLMs) offer strong performance but suffer from high memory and computation costs. Weight binarization provides extreme efficiency, yet existing binary methods designed for dense LLMs struggle with MoE-specific issues, including cross-expert redundancy, task-agnostic importance estimation, and quantization-induced routing shifts. To this end, we propose […]
EgoSelf: From Memory to Personalized Egocentric Assistant
arXiv:2604.19564v1 Announce Type: cross Abstract: Egocentric assistants often rely on first-person view data to capture user behavior and context for personalized services. Since different users exhibit distinct habits, preferences, and routines, such personalization is essential for truly effective assistance. However, effectively integrating long-term user data for personalization remains a key challenge. To address this, we […]
Do LLMs Encode Functional Importance of Reasoning Tokens?
arXiv:2601.03066v3 Announce Type: replace-cross Abstract: Large language models solve complex tasks by generating long reasoning chains, achieving higher accuracy at the cost of increased computational cost and reduced ability to isolate functionally relevant reasoning. Prior work on compact reasoning shortens such chains through probabilistic sampling, heuristics, or supervision from frontier models, but offers limited insight […]
Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems
arXiv:2604.19540v1 Announce Type: cross Abstract: Teams of LLM agents increasingly collaborate on tasks spanning days or weeks: multi-day data-generation sprints where generator, reviewer, and auditor agents coordinate in real time on overlapping batches; specialists carrying findings forward across session restarts; product decisions compounding over many review rounds. This requires agents to share, evaluate, and combine […]
Revisiting RaBitQ and TurboQuant: A Symmetric Comparison of Methods, Theory, and Experiments
arXiv:2604.19528v1 Announce Type: cross Abstract: This technical note revisits the relationship between RaBitQ and TurboQuant under a unified comparison framework. We compare the two methods in terms of methodology, theoretical guarantees, and empirical performance, using a reproducible, transparent, and symmetric setup. Our results show that, despite the claimed advantage of TurboQuant, TurboQuant does not provide […]
OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration
arXiv:2505.11765v3 Announce Type: replace-cross Abstract: Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recently, Multi-Agent Systems (MAS), wherein multiple agents collaborate and communicate with each other, have exhibited enhanced capabilities in complex tasks, such as high-quality code generation and arithmetic reasoning. However, the development of such systems […]
BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps
arXiv:2604.19532v1 Announce Type: cross Abstract: Tokenizing music to fit the general framework of language models is a compelling challenge, especially considering the diverse symbolic structures in which music can be represented (e.g., sequences, grids, and graphs). To date, most approaches tokenize symbolic music as sequences of musical events, such as onsets, pitches, time shifts, or […]
STaD: Scaffolded Task Design for Identifying Compositional Skill Gaps in LLMs
arXiv:2604.18177v2 Announce Type: replace-cross Abstract: Benchmarks are often used as a standard to understand LLM capabilities in different domains. However, aggregate benchmark scores provide limited insight into compositional skill gaps of LLMs and how to improve them. To make these weaknesses visible, we propose Scaffolded Task Design (STaD) framework. STaD generates controlled variations of benchmark […]
Cyber Defense Benchmark: Agentic Threat Hunting Evaluation for LLMs in SecOps
arXiv:2604.19533v1 Announce Type: cross Abstract: We introduce the Cyber Defense Benchmark, a benchmark for measuring how well large language model (LLM) agents perform the core SOC analyst task of threat hunting: given a database of raw Windows event logs with no guided questions or hints, identify the exact timestamps of malicious events. The benchmark wraps […]