OpenRaiser/Intern-Atlas huggingface dataset:
ApET: Approximation-Error Guided Token Compression for Efficient VLMs
Venue: arXiv cs.CV 2026
Year: 2026

Recent Vision-Language Models (VLMs) have demonstrated remarkable multimodal understanding capabilities, yet the redundant visual tokens incur prohibitive computational overhead and degrade inference efficiency. Prior studies typically relies on [CLS] attention or text-vision cross-attention to identify and discard redundant visual tokens. Despite promising results, such solutions are prone to introduce positional bias and, more critically, are incompatible with efficient attention kernels such as FlashAttention, limiting their practical deployment for VLM acceleration. In this paper, we step away from attention dependencies and revisit visual token compression from an information-theoretic perspective, aiming to maximally preserve visual information without any attention involvement. We present ApET, an Approximation-Error guided Token compression framework. ApET first reconstructs the original visual tokens with a small set of basis tokens via linear approximation, then leverages the approximation error to identify and drop the least informative tokens. Extensive experiments across multiple VLMs and benchmarks demonstrate that ApET retains 95.2% of the original performance on image-understanding tasks and even attains 100.4% on video-understanding tasks, while compressing the token budgets by 88.9% and 87.5%, respectively. Thanks to its attention-free design, ApET seamlessly integrates with FlashAttention, enabling further inference acceleration and making VLM deployment more practical. Code is available at https://github.com/MaQianKun0/ApET.
GPU-Native Compressed Neighbor Lists with a Space-Filling-Curve Data Layout
Venue: arXiv cs.CE 2026
Year: 2026

We have developed a compressed neighbor list for short-range particle-particle interaction based on a space- filling curve (SFC) memory layout and particle clusters. The neighbor list can be constructed efficiently on GPUs, supporting NVIDIA and AMD hardware, and has a memory footprint of only 4 bytes per particle to store approximately 200 neighbors. Compared to the highly-optimized domain-specific neighbor list implementation of GROMACS, a molecular dynamics code, it has a comparable cluster overhead and delivers similar performance in a neighborhood pass. Thanks to the SFC-based data layout and the support for varying interaction radii per particle, our neighbor list performs well for systems with high density contrasts, such as those encountered in many astrophysical and cosmological applications. Due to the close relation between SFCs and octrees, our neighbor list seamlessly integrates with octree-based domain decomposition and multipole-based methods for long-range gravitational or electrostatic interactions. To demonstrate the coupling between long- and short-range forces, we simulate an Evrard collapse, a standard test case for the coupling between hydrodynamical and gravitational forces, on up to 1024 GPUs, and compare our results to the analytical solution.
Denotational Semantics for ODRL: Knowledge-Based Constraint Conflict Detection
Venue: arXiv cs.CL 2026
Year: 2026

ODRL's six set-based operators -- isA, isPartOf, hasPart, isAnyOf, isAllOf, isNoneOf -- depend on external domain knowledge that the W3C specification leaves unspecified. Without it, every cross-dataspace policy comparison defaults to Unknown. We present a denotational semantics that maps each ODRL constraint to the set of knowledge-base concepts satisfying it. Conflict detection reduces to denotation intersection under a three-valued verdict -- Conflict, Compatible, or Unknown -- that is sound under incomplete knowledge. The framework covers all three ODRL composition modes (and, or, xone) and all three semantic domains arising in practice: taxonomic (class subsumption), mereological (part-whole containment), and nominal (identity). For cross-dataspace interoperability, we define order-preserving alignments between knowledge bases and prove two guarantees: conflicts are preserved across different KB standards, and unmapped concepts degrade gracefully to Unknown -- never to false conflicts. A runtime soundness theorem ensures that design-time verdicts hold for all execution contexts. The encoding stays within the decidable EPR fragment of first-order logic. We validate it with 154 benchmarks across six knowledge base families (GeoNames, ISO 3166, W3C DPV, a GDPR-derived taxonomy, BCP 47, and ISO 639-3) and four structural KBs targeting adversarial edge cases. Both the Vampire theorem prover and the Z3 SMT solver agree on all 154 verdicts. A key finding is that exclusive composition (xone) requires strictly stronger KB axioms than conjunction or disjunction: open-world semantics blocks exclusivity even when positive evidence appears to satisfy exactly one branch.
Monocular Mesh Recovery and Body Measurement of Female Saanen Goats
Venue: arXiv cs.CV 2026
Year: 2026

The lactation performance of Saanen dairy goats, renowned for their high milk yield, is intrinsically linked to their body size, making accurate 3D body measurement essential for assessing milk production potential, yet existing reconstruction methods lack goat-specific authentic 3D data. To address this limitation, we establish the FemaleSaanenGoat dataset containing synchronized eight-view RGBD videos of 55 female Saanen goats (6-18 months). Using multi-view DynamicFusion, we fuse noisy, non-rigid point cloud sequences into high-fidelity 3D scans, overcoming challenges from irregular surfaces and rapid movement. Based on these scans, we develop SaanenGoat, a parametric 3D shape model specifically designed for female Saanen goats. This model features a refined template with 41 skeletal joints and enhanced udder representation, registered with our scan data. A comprehensive shape space constructed from 48 goats enables precise representation of diverse individual variations. With the help of SaanenGoat model, we get high-precision 3D reconstruction from single-view RGBD input, and achieve automated measurement of six critical body dimensions: body length, height, chest width, chest girth, hip width, and hip height. Experimental results demonstrate the superior accuracy of our method in both 3D reconstruction and body measurement, presenting a novel paradigm for large-scale 3D vision applications in precision livestock farming.
ExpPortrait: Expressive Portrait Generation via Personalized Representation
Venue: arXiv cs.CV 2026
Year: 2026

While diffusion models have shown great potential in portrait generation, generating expressive, coherent, and controllable cinematic portrait videos remains a significant challenge. Existing intermediate signals for portrait generation, such as 2D landmarks and parametric models, have limited disentanglement capabilities and cannot express personalized details due to their sparse or low-rank representation. Therefore, existing methods based on these models struggle to accurately preserve subject identity and expressions, hindering the generation of highly expressive portrait videos. To overcome these limitations, we propose a high-fidelity personalized head representation that more effectively disentangles expression and identity. This representation captures both static, subject-specific global geometry and dynamic, expression-related details. Furthermore, we introduce an expression transfer module to achieve personalized transfer of head pose and expression details between different identities. We use this sophisticated and highly expressive head model as a conditional signal to train a diffusion transformer (DiT)-based generator to synthesize richly detailed portrait videos. Extensive experiments on self- and cross-reenactment tasks demonstrate that our method outperforms previous models in terms of identity preservation, expression accuracy, and temporal stability, particularly in capturing fine-grained details of complex motion.
Uncertainty-Aware Rank-One MIMO Q Network Framework for Accelerated Offline Reinforcement Learning
Venue: arXiv cs.LG 2026
Year: 2026

Offline reinforcement learning (RL) has garnered significant interest due to its safe and easily scalable paradigm. However, training under this paradigm presents its own challenge: the extrapolation error stemming from out-of-distribution (OOD) data. Existing methodologies have endeavored to address this issue through means like penalizing OOD Q-values or imposing similarity constraints on the learned policy and the behavior policy. Nonetheless, these approaches are often beset by limitations such as being overly conservative in utilizing OOD data, imprecise OOD data characterization, and significant computational overhead. To address these challenges, this paper introduces an Uncertainty-Aware Rank-One Multi-Input Multi-Output (MIMO) Q Network framework. The framework aims to enhance Offline Reinforcement Learning by fully leveraging the potential of OOD data while still ensuring efficiency in the learning process. Specifically, the framework quantifies data uncertainty and harnesses it in the training losses, aiming to train a policy that maximizes the lower confidence bound of the corresponding Q-function. Furthermore, a Rank-One MIMO architecture is introduced to model the uncertainty-aware Q-function, \TP{offering the same ability for uncertainty quantification as an ensemble of networks but with a cost nearly equivalent to that of a single network}. Consequently, this framework strikes a harmonious balance between precision, speed, and memory efficiency, culminating in improved overall performance. Extensive experimentation on the D4RL benchmark demonstrates that the framework attains state-of-the-art performance while remaining computationally efficient. By incorporating the concept of uncertainty quantification, our framework offers a promising avenue to alleviate extrapolation errors and enhance the efficiency of offline RL.
Parallelism and Adaptivity in Student-Teacher Witnessing
Venue: arXiv cs.CC 2026
Year: 2026

Student-Teacher Games are a model of computation in which a computationally restricted Student attempts to produce a string satisfying a refutable property, while an all-powerful Teacher refutes incorrect candidates by providing counterexamples. By the classical result of Krajíček, Pudlák, and Takeuti [KPT90], such games capture the witnessing of $\forall\exists\forall$-formulas in bounded arithmetic. In this paper, we introduce subclasses of total search problems in the polynomial hierarchy, classified by the number of rounds and candidate answers per round required for a Student at the corresponding level to solve them. Assuming the polynomial hierarchy does not collapse, we separate these classes for different number of rounds and queries. As applications we obtain the following results: (a) We study theories of bounded arithmetic axiomatized by fine-grained variants of length induction and bounded collection. We prove a general witnessing theorem connecting their $\forall\exists\forall$-consequences to the appropriate classes of Student-Teacher Games. Under the non-collapse of the polynomial hierarchy, we separate these theories. (b) These conditional separations resolve two open problems in bounded arithmetic: one by Buss and Ressayre [Bus85, CK93] on the strength of bounded collection, and one by Pollett [Pol97] on the difference between strict and non-strict double length induction. (c) Finally, we extend the unprovability of circuit upper bounds due to Krajíček and Oliveira [KO17] to the theory $PV_1+BB(Σ^b_1)$, and the unprovability of strong co-nondeterministic circuit lower bounds due to Pich and Santhanam [PS21] to the theory $PV_1+LLIND(sΣ^b_1)$. By the preceding separations, both theories strictly extend $PV_1$ assuming $NP\nsubseteq P/poly$.
Sparse Masked Attention Policies for Reliable Generalization
Venue: arXiv cs.LG 2026
Year: 2026

In reinforcement learning, abstraction methods that remove unnecessary information from the observation are commonly used to learn policies which generalize better to unseen tasks. However, these methods often overlook a crucial weakness: the function which extracts the reduced-information representation has unknown generalization ability in unseen observations. In this paper, we address this problem by presenting an information removal method which more reliably generalizes to new states. We accomplish this by using a learned masking function which operates on, and is integrated with, the attention weights within an attention-based policy network. We demonstrate that our method significantly improves policy generalization to unseen tasks in the Procgen benchmark compared to standard PPO and masking approaches.
Contextual Safety Reasoning and Grounding for Open-World Robots
Venue: arXiv cs.RO 2026
Year: 2026

Robots are increasingly operating in open-world environments where safe behavior depends on context: the same hallway may require different navigation strategies when crowded versus empty, or during an emergency versus normal operations. Traditional safety approaches enforce fixed constraints in user-specified contexts, limiting their ability to handle the open-ended contextual variability of real-world deployment. We address this gap via CORE, a safety framework that enables online contextual reasoning, grounding, and enforcement without prior knowledge of the environment (e.g., maps or safety specifications). CORE uses a vision-language model (VLM) to continuously reason about context-dependent safety rules directly from visual observations, grounds these rules in the physical environment, and enforces the resulting spatially-defined safe sets via control barrier functions. We provide probabilistic safety guarantees for CORE that account for perceptual uncertainty, and we demonstrate through simulation and real-world experiments that CORE enforces contextually appropriate behavior in unseen environments, significantly outperforming prior semantic safety methods that lack online contextual reasoning. Ablation studies validate our theoretical guarantees and underscore the importance of both VLM-based reasoning and spatial grounding for enforcing contextual safety in novel settings. We provide additional resources at https://zacravichandran.github.io/CORE.
A Context-Aware Knowledge Graph Platform for Stream Processing in Industrial IoT
Venue: arXiv cs.DB 2026
Year: 2026

Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink for real-time processing, while SPARQL and SWRL-based reasoning provide context-dependent stream discovery. Experimental evaluations demonstrate the effectiveness of combining semantic models, context-aware reasoning and distributed stream processing to enable interoperable data workflows for Industry 5.0 environments.
A Mixed-Method Framework for Evaluating the Social Impact of Community Cooperation Projects in Developing Countries
Venue: arXiv cs.SI 2026
Year: 2026

Why do some community-cooperation projects catalyse participation through durable, resilient collaboration networks while others result in negligible impact and leave the local social fabric unchanged? We argue outcomes hinge on participation architecture: simple, visible routines -- onboarding help, templated tasks, lightweight contribution/benefit tracking -- that create easy ``entry portals'' and route work across clusters without heavy hierarchy. We introduce Project Intervention Response Analysis (PIRA), a mixed anthropological-network-analysis framework that compares observed community networks with counterfactual networks absent from project-induced ties. PIRA also adds a new egocentric metric to detect ``architectural alters'' -- latent facilitators and boundary spanners. We begin validating PIRA in a three-month field study in Pomerini, Tanzania, where NGOs coordinated citizens, associations, and specialists. Findings indicate that sociotechnical participation architectures -- not charismatic hubs -- underwrite durable coordination. PIRA offers a reusable method to link organizational design mechanisms to formal network signatures.
QUIETT: Query-Independent Table Transformation for Robust Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

Real-world tables often exhibit irregular schemas, heterogeneous value formats, and implicit relational structure, which degrade the reliability of downstream table reasoning and question answering. Most existing approaches address these issues in a query-dependent manner, entangling table cleanup with reasoning and thus limiting generalization. We introduce QuIeTT, a query-independent table transformation framework that preprocesses raw tables into a single SQL-ready canonical representation before any test-time queries are observed. QuIeTT performs lossless schema and value normalization, exposes implicit relations, and preserves full provenance via raw table snapshots. By decoupling table transformation from reasoning, QuIeTT enables cleaner, more reliable, and highly efficient querying without modifying downstream models. Experiments on four benchmarks, WikiTQ, HiTab, NQ-Table, and SequentialQA show consistent gains across models and reasoning paradigms, with particularly strong improvements on a challenge set of structurally diverse, unseen questions.
CodeCompass: Navigating the Navigation Paradox in Agentic Code Intelligence
Venue: arXiv cs.AI 2026
Year: 2026

Modern code intelligence agents operate in contexts exceeding 1 million tokens--far beyond the scale where humans manually locate relevant files. Yet agents consistently fail to discover architecturally critical files when solving real-world coding tasks. We identify the Navigation Paradox: agents perform poorly not due to context limits, but because navigation and retrieval are fundamentally distinct problems. Through 258 automated trials across 30 benchmark tasks on a production FastAPI repository, we demonstrate that graph-based structural navigation via CodeCompass--a Model Context Protocol server exposing dependency graphs--achieves 99.4% task completion on hidden-dependency tasks, a 23.2 percentage-point improvement over vanilla agents (76.2%) and 21.2 points over BM25 retrieval (78.2%).However, we uncover a critical adoption gap: 58% of trials with graph access made zero tool calls, and agents required explicit prompt engineering to adopt the tool consistently. Our findings reveal that the bottleneck is not tool availability but behavioral alignment--agents must be explicitly guided to leverage structural context over lexical heuristics. We contribute: (1) a task taxonomy distinguishing semantic-search, structural, and hidden-dependency scenarios; (2) empirical evidence that graph navigation outperforms retrieval when dependencies lack lexical overlap; and (3) open-source infrastructure for reproducible evaluation of navigation tools.
Hydrodynamic Performance Enhancement of Unmanned Underwater Gliders with Soft Robotic Morphing Wings for Agility Improvement
Venue: arXiv cs.RO 2026
Year: 2026

This work assesses the hydrodynamic efficiency of Underwater Unmanned Vehicles (UUVs) equipped with soft morphing wings compared to conventional rigid wings. Unlike rigid wings, deformable counterparts can alter their aerodynamic properties on demand. Improvements in hydrodynamic efficiency extend a UUV's operational range and may determine mission feasibility. Structural and Computational Fluid Dynamics (CFD) simulations were conducted for both a soft morphing wing and a UUV incorporating it. The results show that a UUV employing soft wings achieves 9.75 percent higher overall efficiency than an equivalent vehicle with traditional rigid wings. These findings confirm the potential of soft robotics to enhance underwater vehicle performance, particularly in applications requiring pressure-agnostic operation.
How Retrieved Context Shapes Internal Representations in RAG
Venue: arXiv cs.CL 2026
Year: 2026

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by conditioning generation on retrieved external documents, but the effect of retrieved context is often non-trivial. In realistic retrieval settings, the retrieved document set often contains a mixture of documents that vary in relevance and usefulness. While prior work has largely examined these phenomena through output behavior, little is known about how retrieved context shapes the internal representations that mediate information integration in RAG. In this work, we study RAG through the lens of latent representations. We systematically analyze how different types of retrieved documents affect the hidden states of LLMs, and how these internal representation shifts relate to downstream generation behavior. Across four question-answering datasets and three LLMs, we analyze internal representations under controlled single- and multi-document settings. Our results reveal how context relevancy and layer-wise processing influence internal representations, providing explanations on LLMs output behaviors and insights for RAG system design.
CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching
Venue: arXiv cs.AI 2026
Year: 2026

As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather than spurious correlations. However, strong performance on traditional reasoning benchmarks does not guarantee true causal reasoning ability of LLMs, as high accuracy may still arise from memorizing semantic patterns instead of analyzing the underlying true causal structures. To bridge this critical gap, we propose a new causal reasoning benchmark, CausalFlip, designed to encourage the development of new LLM paradigm or training algorithms that ground LLM reasoning in causality rather than semantic correlation. CausalFlip consists of causal judgment questions built over event triples that could form different confounder, chain, and collider relations. Based on this, for each event triple, we construct pairs of semantically similar questions that reuse the same events but yield opposite causal answers, where models that rely heavily on semantic matching are systematically driven toward incorrect predictions. To further probe models' reliance on semantic patterns, we introduce a noisy-prefix evaluation that prepends causally irrelevant text before intermediate causal reasoning steps without altering the underlying causal relations or the logic of the reasoning process. We evaluate LLMs under multiple training paradigms, including answer-only training, explicit Chain-of-Thought (CoT) supervision, and a proposed internalized causal reasoning approach that aims to mitigate explicit reliance on correlation in the reasoning process. Our results show that explicit CoT can still be misled by spurious semantic correlations, where internalizing reasoning steps yields substantially improved causal grounding, suggesting that it is promising to better elicit the latent causal reasoning capabilities of base LLMs.