2026-W30

This paper proposes LDT-Coord, a novel framework for coordinating heterogeneous LLM-powered embodied agents. It utilizes lightweight digital twins to enable communication-efficient coordination by having agents independently select actions and report them, overcoming challenges of high communication overhead, LLM capability constraints, and action delays inherent in traditional dialogue-based methods. The core contribution is a more scalable and efficient coordination mechanism for complex embodied agent teams.

This paper introduces AutoWorldBuilder, a multi-agent LLM system for fictional worldbuilding. Its core method involves a hierarchical context compression technique to manage information growth and an iterative review process with specialized agents to ensure content quality and consistency. The main contribution is a robust framework that overcomes LLM limitations in complex, creative generation tasks like worldbuilding.

This paper introduces LongMedBench, a benchmark designed to evaluate medical AI agents on long-horizon clinical decision-making using real-world electronic health records. Unlike previous benchmarks focusing on short-term tasks, LongMedBench simulates longitudinal patient care, requiring agents to integrate information across multiple visits and evolving treatments. Its contribution lies in providing a realistic environment for assessing agents' ability to handle complex, time-dependent clinical scenarios.

This paper investigates "reward hacking" in reinforcement learning for multimodal large language models (MLLMs), where models achieve higher rewards without actually improving task performance. The core method involves introducing the "Newly Rewarded Failure Rate" (NRFR) to quantify failures introduced by RL. The key contribution is demonstrating that outcome-only rewards lead to significant hacking, even in large models, and that answer-aware rewards are crucial for robust alignment.

Mach-Mind-4-Flash is a 35B-parameter MoE model that achieves competitive performance with larger models through post-training optimization and scalable agentic reinforcement learning. Its core contribution lies in a novel training pipeline that includes an efficient RL/OPD infrastructure, parallel domain-specific expert training fused via Multi-Teacher On-Policy Distillation to avoid reward degradation, and Hybrid Median-length Policy Optimization. This approach significantly boosts performance on real-world tasks by enabling scalable agentic interaction environments.

This paper introduces the Associative Recurrent Memory Transformer (ARMT) to overcome the context length limitations of standard transformers. ARMT achieves constant memory scaling and improved efficiency by incorporating an associative recurrent memory mechanism. The authors also contribute domain-specific long-context datasets and a training recipe that combines pre-training, synthetic data, and curriculum learning to effectively extend LLM context.

This paper introduces Proxy-guided Update Signal Transfer (PUST), a modular post-training framework for LLMs. PUST decouples exploration from alignment by using a lightweight proxy model to efficiently discover high-reward behaviors. The relative improvement signal from the proxy is then transferred to guide the primary LLM's policy, enabling reusable and transferable optimization signals.

This paper introduces Xiaomi-Robotics-U0, a unified 38-billion-parameter model for embodied AI. Its core method is to treat embodied generation as an extension of existing foundation image/video models, jointly optimizing various tasks like scene and video generation. The key contribution is a framework that preserves the broad knowledge of foundation models while adapting them to robot-specific constraints, enabling high-quality multi-view embodied synthesis.

HyperSafe addresses the fragility of safety in fine-tuned language models by proposing a post-hoc, model-specific approach. It generates a "Safe Side Network" (SSN) for each fine-tuned model by learning a mapping from layer-wise activation fingerprints to SSN parameters. This allows for non-invasive safety restoration without retraining or affecting task performance.

GEIS introduces a novel Generation-Evaluation-Improvement loop for long-form article generation. Its core method involves a structured cycle of agent skills, including planning, drafting, and evaluation, to address challenges like long context and complex instructions. The key contribution is a declarative and inspectable skill-based system that allows for iterative improvement of agent capabilities through structured feedback and rule-level patching.

This paper addresses the challenge of evaluating LLM agents when no reliable metric exists. Their core method, "Double Ratchet," co-evolves evaluation metrics and agent skills simultaneously. This allows the system to learn and refine both its performance and how to measure it, leading to self-improvement even in novel domains.

AgentCompass provides a unified, open-source infrastructure for evaluating LLM-based agents by decoupling benchmarks, harnesses, and environments. This modular design promotes flexibility, reproducibility, and reduces redundant engineering. Its key contribution lies in offering a scalable and transparent system for diagnosing agent failures and advancing agent research.

This paper introduces MEDA, an LLM- and symbolic regression-powered agentic system for discovering Ordinary Differential Equation (ODE) models of biological systems. MEDA leverages LLMs to retrieve domain knowledge, define variables, and generate constraints, which then guide symbolic regression in proposing and refining candidate ODEs. Its core contribution is an automated framework for mechanistic ODE discovery specifically tailored for complex biological dynamics.

This paper introduces the Experience Memory Graph (EMG) to address error correction in LLM agents. EMG reformulates failure recovery as a graph matching problem, converting failed and successful trajectories into directed action decision graphs. By matching these graphs, it identifies successful workflows and provides explicit correction paths, enabling one-shot error recovery without costly iterative reflection.

This paper addresses untruthfulness in multimodal LLMs by proposing Groc-PO. Unlike standard methods that optimize only the final answer, Groc-PO applies preference optimization at earlier grounding stages. This grounded approach aims to prevent error propagation by providing more direct supervision for multimodal reasoning, leading to more faithful and reliable LLM outputs.

This paper introduces MemCon, a framework that treats LLM agent memory management as a learned, adaptive process. Instead of fixed heuristics, MemCon uses a Markov Decision Process to train an online policy that dynamically decides when, what, and how to retrieve or inject information into memory based on the current task context. This adaptive approach addresses the bottleneck of static memory access, enabling more efficient and effective experience accumulation for LLM agents.

This paper introduces STOCKTAKE, a benchmark for evaluating LLM agents on complex, multi-week decision tasks where the true state of the world is hidden. STOCKTAKE's core method uses a "fair oracle" (an exact Bayes filter) to provide a perfect reference policy that observes the same limited information as the agent. This allows the paper to measure the "knowing-doing gap" – the difference between an agent's understanding of the situation and its ability to act effectively – by comparing its performance to the oracle and analyzing its reasoning.

TRACE addresses the challenge of assigning credit to individual actions in long-horizon multi-turn agents. It achieves this by representing agent rollouts as state transitions and using a frozen reference model to estimate the value of reaching the correct final answer. Per-action rewards are then derived from the temporal difference in these estimated values, providing denser and more informative supervision than outcome-only rewards.

SPyCE proposes a novel framework for multimodal agents that learns reusable skills alongside the policy during reinforcement learning. Instead of treating trajectories as mere rewards or relying on static memory retrieval, SPyCE distills complex reasoning into a hierarchical library of execution and workflow skills. This co-evolutionary approach allows agents to efficiently discover and leverage tool-use patterns across tasks.

This paper introduces "Digital Pantheon," a novel multi-agent framework for simulating political coalition formation using LLMs. It combines SFT, DPO, and RAG to create partisan agents that are both ideologically aligned and factually grounded. The framework's contribution lies in enabling realistic, interpretable simulations of complex political negotiations, demonstrated on a real-world election scenario.

This paper introduces a novel reinforcement learning method for Masked Diffusion Language Models (MDLMs) by treating generation as a two-stage action Markov Decision Process. This approach decomposes the policy gradient into token prediction and masking decisions, allowing for joint optimization. The method achieves state-of-the-art results on mathematical reasoning and coding tasks by effectively addressing the intractability of log-likelihood estimation in MDLMs.

This paper introduces SearchOS-V1, a multi-agent framework for robust open-domain information seeking. Its core method is to represent search progress as explicit, shared state, moving beyond the limitations of implicit tracking in current systems. This explicit state management, through components like Frontier Task and an Evidence Graph, helps agents avoid repetitive loops and improve the quality and completeness of their search results.

Agora enhances LLM agent reasoning by using an auction mechanism to dynamically allocate tasks to expert models and tools. This approach allows agents to bid based on their actual capabilities, ensuring tasks go to the most competent solver and improving cost-efficiency. The paper's contribution lies in this novel incentive-compatible auction framework, which demonstrably improves performance and offers a controllable cost-quality trade-off.

This paper treats LLM coding agent failures not as a single event, but as a dynamic process. Its core method involves analyzing the step-by-step evolution of failures across thousands of execution trajectories. The main contribution is a new framework for understanding how failures emerge, worsen, and become unrecoverable, offering deeper insights than previous studies that focused only on the final outcome.

This paper investigates whether Large Language Models (LLMs) exhibit geopolitical bias when evaluating policies. The core method involves an endorsement experiment where LLMs rate identical policies described as supported by different geopolitical actors (US, EU, China, Russia). The key contribution is the finding that LLMs, particularly Western ones, often rate policies endorsed by the US or EU more favorably than those endorsed by China or Russia, suggesting implicit geopolitical alignment.

This paper proposes a "Self-Guided Test-Time Training" (SG-TTT) method to improve how Large Language Models (LLMs) utilize long contexts. Instead of expensive full-context adaptation or noisy random sampling, SG-TTT intelligently selects relevant spans within the long input for instance-specific parameter tuning. This targeted adaptation significantly enhances LLM performance on long-context tasks by focusing training on useful information.

This paper introduces STEEL, an open-source implementation of FlashAttention optimized for energy-efficient long-sequence inference on AMD's XDNA NPUs. Its core method is a dataflow formulation of prefill attention that exploits spatial parallelism and on-chip memory, coupled with a sparsity-aware pipeline placement to mitigate load imbalance from causal masks. STEEL's contribution is enabling efficient on-device LLM inference for agentic workloads, addressing the challenges of NPU architectural diversity and data movement.

This paper introduces a two-agent system for multimodal question answering, tailored for the QANTA 2026 challenge. The "Tossup" agent uses a smaller GPT model with confidence calibration and numeric reasoning to decide when to answer under uncertainty. The "Bonus" agent employs a larger GPT model with lead-in awareness for accurate answer selection and human adoption.

This paper introduces Super-Tuning, a novel sparse parameter-efficient fine-tuning (PEFT) method. It leverages activation-weighted magnitude scores from a calibration pass to identify crucial parameters for adaptation, reducing memory and compute costs. The method also proposes Supra, a hybrid adapter combining sparse updates with LoRA, achieving state-of-the-art accuracy in arithmetic tasks while maintaining a controlled trainable parameter budget.

This paper introduces AHA, an automated red-teaming method for LLM agents. AHA uses one agent to discover vulnerabilities in another by forming and testing hypotheses about unsafe behavior. Its core contribution is a falsifiable discovery loop that generates reusable vulnerability knowledge, represented in a Vulnerability Concept Graph, to improve agent safety.

This paper proposes a roadmap for developing physical intelligence by addressing fragmentation in current research. It identifies key gaps in model representation, standardization, and system composition, and introduces the concept of an "embodied brain" as a unified target. This embodied brain would integrate multimodal context, compare interventions, and issue action commands, fostering progress towards agents capable of reasoning and acting in the physical world.

RAGU is a modular GraphRAG engine that improves knowledge graph construction by separating extraction from consolidation, using a two-stage process with deduplication and summarization. Its core contribution is a compact, domain-adapted LLM (Meno-Lite-0.1) trained for language skills, which outperforms larger models in knowledge graph construction and matches them on GraphRAG tasks. This approach leads to more accurate and complete knowledge retrieval for LLMs.

This paper investigates how ideological biases in retrieved information affect LLM outputs within Retrieval-Augmented Generation (RAG). Using Lexical Multidimensional Analysis on COVID-19 treatment articles, they identified three ideological discourses. By varying sampling temperatures, the study demonstrates that RAG can transmit, amplify, or suppress these ideological viewpoints in LLM-generated answers.

This paper introduces PaperRouter-Agent, a novel LLM agent designed for personalized hierarchical paper routing. Its core method involves grounding routing decisions in the content of existing papers within user-defined folders, rather than relying solely on folder names. The agent's contribution is a training-free approach that effectively assigns new papers to a user's unique and evolving folder hierarchy by analyzing folder members and incorporating feedback.

This paper investigates whether Reinforcement Learning from Human Feedback (RLHF), specifically Group Relative Policy Optimization (GRPO), improves the performance of small language and vision-language web agents. The study found that GRPO, even with extensive hyperparameter tuning, failed to credibly enhance the success rate of a strong supervised baseline on mastered tasks. Instead, moderate to high learning rates in GRPO actually degraded performance on the text track, suggesting it primarily reshapes existing behavior rather than adding new skills.

This paper proposes Agentic Service-Oriented Computing (ASOC) to address the challenges of integrating LLM-powered agents into complex distributed systems. ASOC advocates for engineering agents as services and orchestrating them using established Service-Oriented Computing principles. Its core contribution is to provide a rigorous, principled approach for building dependable, trustworthy, and secure agent-based systems for enterprise and societal deployment.

This paper investigates if induced emotions can bias Large Language Model (LLM) behavior in sequential decision-making, using the Iowa Gambling Task. The core method involves an imagination-based emotion induction procedure with LLMs. The main contribution is finding that, unlike humans, induced emotions generally do not significantly bias LLM decision-making, though anger shows a conditional effect.

This paper introduces E3 (Estimate, Execute, Expand), a method for AI agents to assess task complexity and optimize resource usage. E3's core is **task-aware execution-scope estimation**, where agents first estimate effort, then execute a minimal path, and only expand if verification fails. This approach significantly reduces computational cost and unnecessary file inspections while maintaining high success rates.

This paper questions the necessity of massive multimodal models for emotion recognition. It proposes Light-MER, a lightweight framework that uses knowledge distillation to transfer capabilities from larger models. This approach achieves competitive or better performance with significantly reduced computational cost, enabling efficient deployment on resource-constrained devices.

This paper introduces the Internet of Agentic Things (IoAT), a framework that unifies AI agents with IoT, cyber-physical systems, and digital twins for closed-loop orchestration. Its core method formalizes this as a coupled workflow-control problem, linking agentic planning with physical execution via a hylomorphic dynamic programming approach. The main contribution is this novel architecture and formalization, enabling autonomous, coordinated action across distributed cyber-physical environments, exemplified by smart building orchestration.

This paper introduces Jetson-PI, a method for real-time robot control using Vision-Language-Action (VLA) models on low-power onboard devices. It tackles the latency and misalignment issues of asynchronous inference by training a lightweight "future correction module" to predict future environment states. This allows the VLA model to act based on foresight, improving control frequency and reducing reaction time.

This paper introduces Elenchos, a framework for evaluating abductive reasoning in LLMs by treating it as an inverse problem. LLMs are tasked with identifying mutations in formal systems and inferring the specific rule changes that caused observed behavioral differences. The study finds that LLMs can often detect system alterations but struggle to pinpoint the exact causes, especially when multiple mutations interact.

This paper introduces MemOps, a novel benchmark for evaluating long-term memory in LLM agents. Instead of just assessing final answers, MemOps reformulates conversational memory as a sequence of explicit lifecycle operations (remembering, forgetting, updating, etc.). This allows for a more granular assessment of memory performance, identifying specific failure points beyond just incorrect outputs.

PalmClaw is a novel framework that enables Large Language Model (LLM) agents to run natively on mobile phones. Its core method involves exposing device capabilities as structured "device tools" that the agent can directly call, overcoming the limitations of traditional GUI-based mobile agents. This allows for more efficient and direct interaction with device features, leading to more powerful on-device task automation.

This paper proposes OAT, an unsupervised method for identifying failure steps in LLM-based agents. OAT models the normal flow of successful task execution using neural controlled differential equations. At inference, it flags steps in a failed trajectory that deviate from this learned normal behavior, enabling lightweight failure attribution without requiring labeled failure data.

This paper introduces ThReadMed-QA, a novel multi-turn dialogue dataset designed to evaluate how well Large Language Models (LLMs) can detect and correct patient misconceptions in medical conversations. The core method involves analyzing LLM performance on this dataset to understand if they can identify and address false beliefs that evolve over multiple turns, a capability not captured by existing evaluation frameworks. The contribution lies in providing a systematic way to assess this crucial aspect of safe medical communication for LLMs.

This paper introduces KnowAct-GUIClaw, a novel framework that enhances agent capabilities for complex GUI automation. Its core method is the "Know Deeply, Act Perfectly" paradigm, which unifies cognitive understanding and operational execution by leveraging accumulated user interaction and task experience. The main contribution is a self-evolving memory and skill mechanism that overcomes OpenClaw's limitations in cross-platform GUI interaction and continuous learning, leading to improved accuracy and efficiency.

This paper introduces CANON, a label-free self-distillation method that leverages consensus among multiple model-generated solutions. Instead of just filtering or rewarding, CANON uses the majority answer as dense, token-level supervision to train a model on its own outputs. This approach extracts richer information from consensus, leading to improved reasoning accuracy without requiring labeled data.

This paper introduces "Deep Interaction," an efficient method for correcting reasoning errors in large language models. Instead of re-generating entire responses, it allows users to directly edit faulty steps in the model's reasoning chain. This edited chain is then refined into a prompt that guides the model to follow the corrected path, significantly improving correction success and reducing token usage.

This paper introduces Safety Sentry, a novel method for LLM agent safety that moves beyond binary safe/unsafe classifications. It reframes intervention as a three-way routing decision (EXECUTE, ASK, REFUSE) for each individual action, considering both the action's inherent risk and the user's context. This approach aims to reduce unnecessary interruptions and improve the accuracy of safety interventions by providing more nuanced and context-aware guidance.

This paper introduces the DVM-HALL model and the NHAS score to address how autonomous AI agents are changing customer loyalty. The DVM-HALL model quantifies brand choice by considering human emotions, AI utility, trust, and verifiable actions, while the NHAS score aims to measure this evolving loyalty in autonomous commerce.

This paper introduces a three-stage calibration framework to analyze how post-training methods (SFT, RL, OPD) affect large language model confidence during chain-of-thought reasoning. The core contribution is demonstrating that different methods excel at different stages: OPD for pre-reasoning difficulty estimation, SFT for early termination, and RL for aggregating reasoning steps. This work highlights the importance of evaluating confidence beyond final accuracy and reveals position-dependent calibration patterns.

This paper introduces a framework for automatically improving LLM agent harnesses (prompts, knowledge, etc.) without modifying the LLM weights. It achieves this by separating the generation of harness changes from their evaluation, using a language model to propose patches and deterministic code for trustworthy measurement and significance testing. The core contribution is a robust method for crediting harness improvements, ensuring they are genuine performance gains rather than artifacts.

This paper introduces ANet Patu-1, a self-organizing consensus protocol for AI agents. It models the value of agent networks based on coordination group size, deriving properties for optimal collaboration. ANet Patu-1 continuously reforms coalitions to achieve $O(1)$ parallel consensus rounds, adaptively capturing the benefits of different network structures. Its contribution lies in demonstrating how emergent, heterogeneous agent groups can achieve compounding collective value as their size increases.

StructureClaw introduces an artifact-centered workbench for LLM agents in structural engineering, enabling them to generate a complete chain of interdependent engineering artifacts. Its core method involves governed skills, typed tools, and local backends to ensure traceability and executability. The contribution is an executable benchmark, StructureClaw-Bench, which rigorously evaluates LLM agents on their ability to complete entire structural engineering workflows, not just generate fluent text.

This paper introduces Symbal, a method for detecting systematic misalignments in MLLM-generated captions, where errors consistently relate to specific visual features. Symbal uses a dual-stage approach with existing foundation models to identify and summarize these recurring captioning errors. The authors also present SymbalBench, a large benchmark dataset for evaluating such detection methods.

This paper introduces BadWAM, a framework for evaluating adversarial attacks on World-Action Models (WAMs). It demonstrates that small visual perturbations can disrupt the crucial alignment between a WAM's predicted future and its actual actions, leading to incorrect execution. BadWAM characterizes these "World-Action Drift Attacks" based on their strength and stealthiness.

ALICE is a novel foundation model for computational pathology that consolidates expertise from multiple specialized models (vision, vision-language, and slide-level) into a single backbone. It achieves this through a multi-stage agglomerative distillation process, trained on a massive dataset of pathology images. ALICE demonstrates superior performance across diverse tasks, showcasing its general-purpose capabilities and the effectiveness of its distillation approach.

This paper introduces the Complex Social Behavior (CSB) dataset to evaluate vision-language models (VLMs) on complex human interactions, moving beyond simpler benchmarks. By analyzing VLMs from 2017-2025 on CSB and MS-COCO, the study tracks accuracy improvements and categorizes visual-cognitive errors, revealing that CSB highlights more significant VLM progress.

This paper proposes a practical method for recovering source code from stripped binary functions. It combines reverse engineering with anchor-based retrieval from a source code database, followed by LLM reasoning to re-rank candidate source code snippets. The core contribution is a binary-to-source matching approach that aims to find exact source functions rather than generating pseudocode, achieving high instruction coverage.

This paper introduces mAIEnergy, a novel multimodal dataset designed to advance Large Language Model applications in the energy sector. It comprises diverse data types, including text, images, time series, and geospatial information, all harmonized and structured for immediate use. The dataset's core contribution is providing a comprehensive, ready-to-use energy knowledge base, facilitating LLM training and research in this critical domain.

This paper introduces an explainable agentic system to detect sophisticated, long-term conversational scams, which current message-level detectors miss. Its core method utilizes a summary-based memory to track scam progression. The key contribution is a novel benchmark, ConScamBench-278, for evaluating such systems and demonstrating high performance across various scam types.

This paper introduces the Causal Discovery Foundation Model (CDFM), a unified framework for general-purpose causal discovery. CDFM aims to overcome the limitations of dataset-specific algorithms by enabling zero-shot structural inference across diverse domains. Its core contribution lies in leveraging theoretical insights into causal identifiability and incorporating causal prior mechanisms to achieve reliable generalization.

DiffEEG is a self-supervised denoising diffusion model that learns generic EEG representations from unlabeled data. It addresses annotation scarcity and class imbalance in seizure detection by pre-training a 1D U-Net with self-attention and then fine-tuning with reinforcement learning to directly optimize for seizure detection metrics like F1-score. This approach significantly improves seizure detection and subtyping performance, particularly for rare seizure events.

This paper introduces E-VQA, a new task that requires Video LLMs to provide not just answers but also precise spatio-temporal evidence (temporal segments and object masks) to support their responses. The core contribution is the creation of the ST-Evidence benchmark and a large-scale instruction dataset to train models that can achieve verifiable visual grounding, addressing the current "black box" nature of Video LLMs and revealing a disconnect between answering ability and true visual understanding.

HCRMap addresses the challenge of imbalanced expert usage in Mixture-of-Experts (MoE) models on 3.5D chiplet systems. Its core method dynamically manages expert replicas by considering expert hotness, costs, and resource pressure to decide which experts to promote, retain, demote, or evict. The contribution is a pressure-aware framework that maps tokens to suitable resident replicas, mitigating compute imbalance and communication pressure.

This paper introduces Pythia, a novel multi-agent system for autonomous clinical symptom detection. Pythia's core method involves automatically generating and optimizing extraction prompts for clinical concepts, eliminating the need for manual prompt engineering or model fine-tuning. Its main contribution is demonstrating that Pythia can achieve high accuracy in identifying signs and symptoms from clinical notes, while also ensuring data privacy by operating on local infrastructure.

This paper introduces HealthClaw, a novel self-evolving agent for longitudinal personal health management. Its core method involves separating static medical knowledge from a dynamic, private memory that learns and updates over time. HealthClaw's key contribution is demonstrating significantly improved accuracy and reduced context exposure in health management tasks by adapting to individual changes, while also enhancing privacy.

This paper analyzes agentic coding tool adoption in GitHub projects by examining over 25,000 agent-generated pull requests. It finds that intensive adoption is rare, concentrated in a few projects, and surprisingly, smaller projects show higher participation and activity ratios. The core contribution is understanding how these tools are integrated and managed at the project level, revealing nuanced adoption patterns beyond just the PR outcome.

This paper proposes DynaBase, a minimal, interpretable architecture for zero-shot dynamical system reconstruction. By iteratively simplifying a state-of-the-art model, they arrive at a two-parameter system that forecasts by linearly blending the current latent state with its nearest in-context neighbor and its successor. DynaBase achieves surprisingly competitive performance with drastically fewer parameters, offering insights into the core mechanisms of in-context learning for dynamical systems.

AutoSynthesis is an end-to-end multi-agent system that automates the entire meta-analysis process, from formulating a search strategy to generating a PRISMA-compliant report. Its core method involves a series of agents that handle literature retrieval, screening, data extraction, and statistical analysis, including heterogeneity and risk-of-bias assessments. The paper's contribution is a scalable, automated solution to a traditionally manual and time-consuming task, enabling more efficient evidence synthesis.

This paper benchmarks six multimodal large language models (MLLMs) on a scientific visualization literacy assessment test. The core method involves evaluating MLLMs against human performance on a standardized test covering various visualization techniques and tasks. The key contribution is demonstrating that current MLLMs exhibit uneven scientific visualization literacy, with Gemini showing promising performance while open-source models lag behind human capabilities.

BrainPilot is a fully open-source, multi-agent system designed to automate brain science research. Its core method involves a principal investigator (PI) agent coordinating specialist agents, each grounded in curated domain knowledge, to perform complex research tasks. The system's main contribution is accelerating discovery by providing traceable logs and agent-verified results, addressing limitations of current AI agents in domain expertise and reliability.

FlashDecoder is a novel, pure-Transformer video decoder that achieves real-time, frame-by-frame latent-to-pixel generation. Its core method uses a rolling KV cache to limit attention to a fixed temporal window, ensuring constant latency and memory efficiency regardless of video length. This approach enables high-resolution training and matches convolutional decoder quality while being significantly faster.