OpenRaiser/Intern-Atlas huggingface dataset:
InterDeepResearch: Enabling Human-Agent Collaborative Information Seeking through Interactive Deep Research
Venue: arXiv cs.IR 2026
Year: 2026

Deep research systems powered by LLM agents have transformed complex information seeking by automating the iterative retrieval, filtering, and synthesis of insights from massive-scale web sources. However, existing systems predominantly follow an autonomous "query-to-report" paradigm, limiting users to a passive role and failing to integrate their personal insights, contextual knowledge, and evolving research intents. This paper addresses the lack of human-in-the-loop collaboration in the agentic research process. Through a formative study, we identify that current systems hinder effective human-agent collaboration in terms of process observability, real-time steerability, and context navigation efficiency. Informed by these findings, we propose InterDeepResearch, an interactive deep research system backed by a dedicated research context management framework. The framework organizes research context into a hierarchical architecture with three levels (information, actions, and sessions), enabling dynamic context reduction to prevent LLM context exhaustion and cross-action backtracing for evidence provenance. Built upon this framework, the system interface integrates three coordinated views for visual sensemaking, and dedicated interaction mechanisms for interactive research context navigation. Evaluation on the Xbench-DeepSearch-v1 and Seal-0 benchmarks shows that InterDeepResearch achieves competitive performance compared to state-of-the-art deep research systems, while a formal user study demonstrates its effectiveness in supporting human-agent collaborative information seeking. Project page with system demo: https://github.com/bopan3/InterDeepResearch.
FastDSAC: Unlocking the Potential of Maximum Entropy RL in High-Dimensional Humanoid Control
Venue: arXiv cs.LG 2026
Year: 2026

Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a formidable challenge, as the ``curse of dimensionality'' induces severe exploration inefficiency and training instability in expansive action spaces. Consequently, recent high-throughput paradigms have largely converged on deterministic policy gradients combined with massive parallel simulation. We challenge this compromise with FastDSAC, a framework that effectively unlocks the potential of maximum entropy stochastic policies for complex continuous control. We introduce Dimension-wise Entropy Modulation (DEM) to dynamically redistribute the exploration budget and enforce diversity, alongside a continuous distributional critic tailored to ensure value fidelity and mitigate high-dimensional value overestimation. Extensive evaluations on HumanoidBench and other continuous control tasks demonstrate that rigorously designed stochastic policies can consistently match or outperform deterministic baselines, achieving notable gains of 180\% and 400\% on the challenging \textit{Basketball} and \textit{Balance Hard} tasks.
When Drafts Evolve: Speculative Decoding Meets Online Learning
Venue: arXiv cs.LG 2026
Year: 2026

Speculative decoding has emerged as a widely adopted paradigm for accelerating large language model inference, where a lightweight draft model rapidly generates candidate tokens that are then verified in parallel by a larger target model. However, due to limited model capacity, drafts often struggle to approximate the target distribution, resulting in shorter acceptance lengths and diminished speedup. A key yet under-explored observation is that speculative decoding inherently provides verification feedback that quantifies the deviation between the draft and target models at no additional cost. This process naturally forms an iterative "draft commits-feedback provides-draft adapts" evolving loop, which precisely matches the online learning paradigm. Motivated by this connection, we propose OnlineSpec, a unified framework that systematically leverages interactive feedback to continuously evolve draft models. Grounded in dynamic regret minimization, we establish a formal link between online learning performance and speculative system's acceleration rate, and develop novel algorithms via modern online learning techniques, including optimistic online learning that adaptively reuses historical gradients as predictive update hints, and online ensemble learning that dynamically maintains multiple draft models. Our algorithms are equipped with theoretical justifications and improved acceleration rates, achieving up to 24% speedup over seven benchmarks and three foundation models.
VLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Models
Venue: arXiv cs.IR 2026
Year: 2026

Multimodal recommendation is commonly framed as a feature fusion problem, where textual and visual signals are combined to better model user preference. However, the effectiveness of multimodal recommendation may depend not only on how modalities are fused, but also on whether item content is represented in a semantic space aligned with preference matching. This issue is particularly important because raw visual features often preserve appearance similarity, while user decisions are typically driven by higher-level semantic factors such as style, material, and usage context. Motivated by this observation, we propose LVLM-grounded Multimodal Semantic Representation for Recommendation (VLM4Rec), a lightweight framework that organizes multimodal item content through semantic alignment rather than direct feature fusion. VLM4Rec first uses a large vision-language model to ground each item image into an explicit natural-language description, and then encodes the grounded semantics into dense item representations for preference-oriented retrieval. Recommendation is subsequently performed through a simple profile-based semantic matching mechanism over historical item embeddings, yielding a practical offline-online decomposition. Extensive experiments on multiple multimodal recommendation datasets show that VLM4Rec consistently improves performance over raw visual features and several fusion-based alternatives, suggesting that representation quality may matter more than fusion complexity in this setting. The code is released at https://github.com/tyvalencia/enhancing-mm-rec-sys.
Collaborative Multi-Agent Optimization for Personalized Memory System
Venue: arXiv cs.MA 2026
Year: 2026

Memory systems are crucial to personalized LLMs by mitigating the context window limitation in capturing long-term user-LLM conversations. Typically, such systems leverage multiple agents to handle multi-granular memory construction and personalized memory retrieval tasks. To optimize the system, existing methods focus on specializing agents on their local tasks independently via prompt engineering or fine-tuning. However, they overlook cross-agent collaboration, where independent optimization on local agents hardly guarantees the global system performance. To address this issue, we propose a Collaborative Reinforcement Learning Framework for Multi-Agent Memory Systems (CoMAM), jointly optimizing local agents to facilitate collaboration. Specifically, we regularize agents' execution as a sequential Markov decision process (MDP) to embed inter-agent dependencies into the state transition, yielding both local task rewards (e.g., information coverage for memory construction) and global rewards (i.e., query-answer accuracy). Then, we quantify each agent's contribution via group-level ranking consistency between local and global rewards, treating them as adaptive weights to assign global credit and integrate local-global rewards. Each agent is optimized by these integrated rewards, aligning local improvements with the global performance. Experiments show CoMAM outperforms leading memory systems, validating the efficacy of our proposed collaborative reinforcement learning for joint optimization.
"I Should Know, But I Dare Not Ask": From Understanding Challenges in Patient Journeys to Deriving Design Implications for North Korean Defectors' Adaptation
Venue: arXiv cs.HC 2026
Year: 2026

While it is known that North Korean defectors (NKDs) struggle with South Korea's healthcare system, the specific challenges of their patient journey remain underexplored. To investigate this, we conducted interviews with 10 NKDs about an 8-step patient journey and identified the clinical consultation step as a critical barrier for all participants, marked by three key challenges: expressing symptoms, managing social and cultural concerns, and overcoming language differences. In response, we developed Medibridge, a mobile prototype that allows users to rehearse with an AI doctor before a real hospital visit to generate a tangible ``Helper Note'' for their actual consultation. Our evaluation with 15 NKDs showed improvements in perceived communication capability, including greater expression clarity, reduced social and cultural concerns, and enhanced linguistic confidence. Our contributions include an empirical understanding of NKDs' healthcare challenges, a novel AI-powered rehearsal system that prepares users for real-world clinical communication, and design implications for inclusive technologies for displaced populations.
ExpanderGraph-128: A Novel Graph-Theoretic Block Cipher with Formal Security Analysis and Hardware Implementation
Venue: arXiv cs.CR 2026
Year: 2026

Lightweight block cipher design has largely focused on incremental optimization of established paradigms such as substitution--permutation networks, Feistel structures, and ARX constructions, where security derives from the algebraic complexity of individual components. We propose a different approach based on \emph{expander-graph interaction networks}, where diffusion and security arise from sparse structural connectivity rather than component sophistication. We present \textbf{ExpanderGraph-128 (EGC128)}, a 128-bit block cipher constructed as a 20-round balanced Feistel network. Each round applies a 64-bit nonlinear transformation governed by a 3-regular expander graph whose vertices execute identical 4-input Boolean functions on local neighborhoods. Security analysis combines MILP-based differential bounds, proven optimal through 10 rounds via SCIP, establishing 147.3-bit differential security and conservatively extrapolating to 413 bits for the full cipher. Linear analysis provides MILP bounds of $\geq 2^{145}$, while related-key evaluation shows no free rounds for any nonzero key difference. Additional tests confirm rapid algebraic degree growth and the absence of invariant affine subspaces. Implementation results demonstrate practical efficiency. FPGA synthesis on Xilinx Artix-7 achieves 261~Mbps at 100~MHz using only 380 LUTs, while ARM Cortex-M4F software requires 25.8~KB Flash and 1.66~ms per encryption. These results show that expander-graph-driven diffusion provides a promising design methodology for lightweight cryptography.
Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw
Venue: arXiv cs.CR 2026
Year: 2026

The rapid evolution of Large Language Models (LLMs) into autonomous, tool-calling agents has fundamentally altered the cybersecurity landscape. Frameworks like OpenClaw grant AI systems operating-system-level permissions and the autonomy to execute complex workflows. This level of access creates unprecedented security challenges. Consequently, traditional content-filtering defenses have become obsolete. This report presents a comprehensive security analysis of the OpenClaw ecosystem. We systematically investigate its current threat landscape, highlighting critical vulnerabilities such as prompt injection-driven Remote Code Execution (RCE), sequential tool attack chains, context amnesia, and supply chain contamination. To systematically contextualize these threats, we propose a novel tri-layered risk taxonomy for autonomous Agents, categorizing vulnerabilities across AI Cognitive, Software Execution, and Information System dimensions. To address these systemic architectural flaws, we introduce the Full-Lifecycle Agent Security Architecture (FASA). This theoretical defense blueprint advocates for zero-trust agentic execution, dynamic intent verification, and cross-layer reasoning-action correlation. Building on this framework, we present Project ClawGuard, our ongoing engineering initiative. This project aims to implement the FASA paradigm and transition autonomous agents from high-risk experimental utilities into trustworthy systems. Our code and dataset are available at https://github.com/NY1024/ClawGuard.
98$\times$ Faster LLM Routing Without a Dedicated GPU: Flash Attention, Prompt Compression, and Near-Streaming for the vLLM Semantic Router
Venue: arXiv cs.CL 2026
Year: 2026

System-level routers that intercept LLM requests for safety classification, domain routing, and PII detection must be both fast and operationally lightweight: they should add minimal latency to every request, yet not require a dedicated GPU -- an expensive resource better used for LLM inference itself. When the router co-locates on the same GPU as vLLM serving instances, standard attention's $O(n^2)$ memory makes long-context classification (8K--32K tokens) impossible: at 8K tokens, three concurrent classifiers need ${\sim}$4.5\,GB for attention masks alone, far exceeding the memory left by vLLM. We present three staged optimizations for the vLLM Semantic Router, benchmarked on AMD Instinct MI300X, that solve both the latency and the memory problem. \emph{Stage~1}: a custom CK Flash Attention operator for ONNX Runtime on ROCm reduces attention memory from $O(n^2)$ to $O(n)$ and end-to-end (E2E) latency from 4{,}918\,ms to 127\,ms (\textbf{38.7$\times$}), enabling 8K--32K tokens where SDPA OOMs. \emph{Stage~2}: classical NLP prompt compression (TextRank, position weighting, TF-IDF, and novelty scoring) reduces all inputs to ${\sim}$512 tokens without neural inference, capping both latency and GPU memory at a constant regardless of original prompt length (E2E 127$\to$62\,ms, \textbf{2.0$\times$}). \emph{Stage~3}: near-streaming body processing with adaptive chunking and zero-copy JSON eliminates serialization overhead (E2E 62$\to$50\,ms, \textbf{1.2$\times$}). Cumulatively: \textbf{98$\times$} improvement (4{,}918\,ms to 50\,ms), 16K-token routing in 108\,ms, and a total router GPU footprint under 800\,MB -- small enough to share a GPU with LLM serving and removing the need for a dedicated accelerator. Stage~1 targets AMD ROCm (NVIDIA GPUs already have FlashAttention via cuDNN); Stages~2 and~3 are hardware-agnostic.
LR-SGS: Robust LiDAR-Reflectance-Guided Salient Gaussian Splatting for Self-Driving Scene Reconstruction
Venue: arXiv cs.CV 2026
Year: 2026

Recent 3D Gaussian Splatting (3DGS) methods have demonstrated the feasibility of self-driving scene reconstruction and novel view synthesis. However, most existing methods either rely solely on cameras or use LiDAR only for Gaussian initialization or depth supervision, while the rich scene information contained in point clouds, such as reflectance, and the complementarity between LiDAR and RGB have not been fully exploited, leading to degradation in challenging self-driving scenes, such as those with high ego-motion and complex lighting. To address these issues, we propose a robust and efficient LiDAR-reflectance-guided Salient Gaussian Splatting method (LR-SGS) for self-driving scenes, which introduces a structure-aware Salient Gaussian representation, initialized from geometric and reflectance feature points extracted from LiDAR and refined through a salient transform and improved density control to capture edge and planar structures. Furthermore, we calibrate LiDAR intensity into reflectance and attach it to each Gaussian as a lighting-invariant material channel, jointly aligned with RGB to enforce boundary consistency. Extensive experiments on the Waymo Open Dataset demonstrate that LR-SGS achieves superior reconstruction performance with fewer Gaussians and shorter training time. In particular, on Complex Lighting scenes, our method surpasses OmniRe by 1.18 dB PSNR.
Evaluation of TCP Congestion Control for Public High-Performance Wide-Area Networks
Venue: arXiv cs.NI 2026
Year: 2026

Practitioners of a growing number of scientific and artificial-intelligence (AI) applications use High-Performance Wide-Area Networks (HP-WANs) for moving massive data sets between remote facilities. Accurate prediction of the flow completion time (FCT) is essential in these data-transfer workflows because compute and storage resources are tightly scheduled and expensive. We assess the viability of three TCP congestion control algorithms (CUBIC, BBRv1, and BBRv3) for massive data transfers over public HP-WANs, where limited control of critical data-path parameters precludes the use of Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCEv2), which is known to outperform TCP in private HP-WANs. Extensive experiments on the FABRIC testbed indicate that the configuration control limitations can also hinder TCP, especially through microburst-induced packet losses. Under these challenging conditions, we show that the highest FCT predictability is achieved by combination of BBRv1 with the application of traffic shaping before the HP-WAN entry points.
Marker-Based 3D Reconstruction of Aggregates with a Comparative Analysis of 2D and 3D Morphologies
Venue: arXiv cs.CV 2026
Year: 2026

Aggregates, serving as the main skeleton in assemblies of construction materials, are important functional components in various building and transportation infrastructures. They can be used in unbound layer applications, e.g. pavement base and railroad ballast, bound applications of cement concrete and asphalt concrete, and as riprap and large-sized primary crushed rocks. Information on the size and shape or morphology of aggregates can greatly facilitate the Quality Assurance/Quality Control (QA/QC) process by providing insights of aggregate behavior during composition and packing. A full 3D characterization of aggregate particle morphology is difficult both during production in a quarry and at a construction site. Many aggregate imaging approaches have been developed to quantify the particle morphology by computer vision, including 2D image-based approaches that analyze particle silhouettes and 3D scanning-based methods that require expensive devices such as 3D laser scanners or X-Ray Computed Tomography (CT) equipment. This paper presents a flexible and cost-effective photogrammetry-based approach for the 3D reconstruction of aggregate particles. The proposed approach follows a marker-based design that enables background suppression, point cloud stitching, and scale referencing to obtain high-quality aggregate models. The accuracy of the reconstruction results was validated against ground-truth for selected aggregate samples. Comparative analyses were conducted on 2D and 3D morphological properties of the selected samples. Significant differences were found between the 2D and 3D statistics. Based on the presented approach, 3D shape information of aggregates can be obtained easily and at a low cost, thus allowing convenient aggregate inspection, data collection, and 3D morphological analysis.
Bin~Wan,G2HFNet: GeoGran-Aware Hierarchical Feature Fusion Network for Salient Object Detection in Optical Remote Sensing Images
Venue: arXiv cs.CV 2026
Year: 2026

Remote sensing images captured from aerial perspectives often exhibit significant scale variations and complex backgrounds, posing challenges for salient object detection (SOD). Existing methods typically extract multi-level features at a single scale using uniform attention mechanisms, leading to suboptimal representations and incomplete detection results. To address these issues, we propose a GeoGran-Aware Hierarchical Feature Fusion Network (G2HFNet) that fully exploits geometric and granular cues in optical remote sensing images. Specifically, G2HFNet adopts Swin Transformer as the backbone to extract multi-level features and integrates three key modules: the multi-scale detail enhancement (MDE) module to handle object scale variations and enrich fine details, the dual-branch geo-gran complementary (DGC) module to jointly capture fine-grained details and positional information in mid-level features, and the deep semantic perception (DSP) module to refine high-level positional cues via self-attention. Additionally, a local-global guidance fusion (LGF) module is introduced to replace traditional convolutions for effective multi-level feature integration. Extensive experiments demonstrate that G2HFNet achieves high-quality saliency maps and significantly improves detection performance in challenging remote sensing scenarios.
Semantic-Aware 6G Network Management through Knowledge-Defined Networking
Venue: arXiv cs.NI 2026
Year: 2026

Semantic communication is emerging as a key paradigm for 6G networks, where the goal is not to perfectly reconstruct bits but to preserve the meaning that matters for a given task. This shift can improve bandwidth efficiency, robustness, and application-level performance. However, most existing studies focus solely on encoder-decoder design and ignore network-wide decision-making. As data traverses multiple hops, semantic relevance may decrease, routing may overlook meaningful information, and semantic distortion can increase under dynamic network conditions. To address these challenges, this paper proposes a management-oriented semantic communication framework built upon Knowledge-Defined Networking (KDN). The framework comprises three core modules: a semantic-reasoning module that computes relevance scores by mapping semantic embeddings onto a knowledge graph that encodes task concepts and contextual relationships; a semantic-aware routing mechanism that forwards data along paths that preserve meaning; and a semantic-distortion controller that adaptively adjusts encoding and routing to preserve semantic fidelity. Our ns-3 results show clear benefits: semantic delivery success improves by 12%, semantic distortion decreases by 22%, re-routing events drop by 44%, and throughput efficiency rises by 14% compared to baseline methods (shortest-path, load-based, and distortion-only routing). These results indicate that meaning-aware and feedback-driven control is essential for reliable and scalable semantic communication in future 6G networks.