OpenRaiser/Intern-Atlas huggingface dataset:
VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models
Venue: arXiv cs.CV 2026
Year: 2026

Advances in large reasoning models have shown strong performance on complex reasoning tasks by scaling test-time compute through extended reasoning. However, recent studies observe that in vision-dependent tasks, extended textual reasoning at inference time can degrade performance as models progressively lose attention to visual tokens and increasingly rely on textual priors alone. To address this, prior works use reinforcement learning (RL)-based fine-tuning to route visual tokens or employ refocusing mechanisms during reasoning. While effective, these methods are computationally expensive, requiring large-scale data generation and policy optimization. To leverage the benefits of test-time compute without additional RL fine-tuning, we propose VisRef, a visually grounded test-time scaling framework. Our key idea is to actively guide the reasoning process by re-injecting a coreset of visual tokens that are semantically relevant to the reasoning context while remaining diverse and globally representative of the image, enabling more grounded multi-modal reasoning. Experiments on three visual reasoning benchmarks with state-of-the-art multi-modal large reasoning models demonstrate that, under fixed test-time compute budgets, VisRef consistently outperforms existing test-time scaling approaches by up to 6.4%.
Towards the Systematic Testing of Regular Expression Engines
Venue: arXiv cs.SE 2026
Year: 2026

Software engineers use regular expressions (regexes) across a wide range of domains and tasks. To support regexes, software projects must integrate a regex engine, whether provided natively by the language runtime (e.g., Python's re) or included as an external dependency (e.g., PCRE). However, these engines may contain bugs and introduce vulnerabilities. A common strategy for testing regex engines involves differential testing -- comparing outputs across different implementations. However, this approach is concerning because regex syntax and semantics vary significantly between dialects (e.g., POSIX vs. PCRE). Fuzzing is also utilized to ease testing of feature-rich regex implementations to expose defects, but naive byte-level mutations generate syntactically invalid inputs that exercise only parsing logic, not matching internals. In this work, we describe our progress towards ReTest, a framework that systematically tests regular expression engines by combining grammar-aware fuzzing for high code coverage with metamorphic testing to generate dialect-independent test oracles. So far, we have surveyed testing practices across 22 regex engines, analyzed 1,007 regex engine bugs and 156 CVEs to characterize failure modes, and curated 16 metamorphic relations for regexes derived from Kleene algebra. Our preliminary evaluation on PCRE shows that ReTest achieves 3x higher edge coverage than existing fuzzing approaches and has identified three new memory safety defects. We conclude by describing our next steps toward our ultimate goal: helping regex engine developers identify bugs without depending on a consistent cross-implementation standard.
Proof-of-Perception: Certified Tool-Using Multimodal Reasoning with Compositional Conformal Guarantees
Venue: arXiv cs.CV 2026
Year: 2026

We present Proof-of-Perception (PoP), a tool-using framework that casts multimodal reasoning as an executable graph with explicit reliability guarantees. Each perception or logic node outputs a conformal set, yielding calibrated, stepwise uncertainty; a lightweight controller uses these certificates to allocate compute under a budget, expanding with extra tool calls only when needed and stopping early otherwise. This grounds answers in verifiable evidence, reduces error compounding and hallucinations, and enables principled accuracy-compute trade-offs. Across document, chart, and multi-image QA benchmarks, PoP improves performance and reliability over strong chain-of-thought, ReAct-style, and program-of-thought baselines while using computation more efficiently.
SPARe: Stacked Parallelism with Adaptive Reordering for Fault-Tolerant LLM Pretraining Systems with 100k+ GPUs
Venue: arXiv cs.DC 2026
Year: 2026

In large-scale LLM pre-training systems with 100k+ GPUs, failures become the norm rather than the exception, and restart costs can dominate wall-clock training time. However, existing fault-tolerance mechanisms are largely unprepared for this restart-dominant regime. To address this challenge, we propose SPARe - Stacked Parallelism with Adaptive Reordering - a fault-tolerance framework that masks node failures during gradient synchronization by stacking redundant data shards across parallelism groups and adaptively reordering execution. SPARe achieves availability comparable to traditional replication while maintaining near-constant computation overhead of only 2~3x, even under high redundancy where traditional replication would require linearly inflating overhead. We derive closed-form expressions for endurable failure count and computation overhead, validate them via SimGrid-based discrete-event simulation, and jointly optimize redundancy and checkpointing to minimize time-to-train. At extreme scale with up to 600k GPUs, SPARe reduces time-to-train by 40~50% compared to traditional replication.
Quantifying Catastrophic Forgetting in IoT Intrusion Detection Systems
Venue: arXiv cs.LG 2026
Year: 2026

Distribution shifts in attack patterns within RPL-based IoT networks pose a critical threat to the reliability and security of large-scale connected systems. Intrusion Detection Systems (IDS) trained on static datasets often fail to generalize to unseen threats and suffer from catastrophic forgetting when updated with new attacks. Ensuring continual adaptability of IDS is therefore essential for maintaining robust IoT network defence. In this focused study, we formulate intrusion detection as a domain continual learning problem and propose a method-agnostic IDS framework that can integrate diverse continual learning strategies. We systematically benchmark five representative approaches across multiple domain-ordering sequences using a comprehensive multi-attack dataset comprising 48 domains. Results show that continual learning mitigates catastrophic forgetting while maintaining a balance between plasticity, stability, and efficiency, a crucial aspect for resource-constrained IoT environments. Among the methods, Replay-based approaches achieve the best overall performance, while Synaptic Intelligence (SI) delivers near-zero forgetting with high training efficiency, demonstrating strong potential for stable and sustainable IDS deployment in dynamic IoT networks.
Distribution-Aware Companding Quantization of Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Large language models such as GPT and Llama are trained with a next-token prediction loss. In this work, we suggest that training language models to predict multiple future tokens at once results in higher sample efficiency. More specifically, at each position in the training corpus, we ask the model to predict the following n tokens using n independent output heads, operating on top of a shared model trunk. Considering multi-token prediction as an auxiliary training task, we measure improved downstream capabilities with no overhead in training time for both code and natural language models. The method is increasingly useful for larger model sizes and keeps its appeal when training for multiple epochs. Gains are especially pronounced on generative benchmarks like coding, where our models consistently outperform strong baselines by several percentage points. Our 13B parameter models solves 12 % more problems on HumanEval and 17 % more on MBPP than comparable next-token models. Experiments on small algorithmic tasks demonstrate that multi-token prediction is favorable for the development of induction heads and algorithmic reasoning capabilities. As an additional benefit, models trained with 4-token prediction are up to 3X times faster at inference, even with large batch sizes.
Policy Compliance of User Requests in Natural Language for AI Systems
Venue: arXiv cs.CL 2026
Year: 2026

Consider an organization whose users send requests in natural language to an AI system that fulfills them by carrying out specific tasks. In this paper, we consider the problem of ensuring such user requests comply with a list of diverse policies determined by the organization with the purpose of guaranteeing the safe and reliable use of the AI system. We propose, to the best of our knowledge, the first benchmark consisting of annotated user requests of diverse compliance with respect to a list of policies. Our benchmark is related to industrial applications in the technology sector. We then use our benchmark to evaluate the performance of various LLM models on policy compliance assessment under different solution methods. We analyze the differences on performance metrics across the models and solution methods, showcasing the challenging nature of our problem.
Improving Full Waveform Inversion in Large Model Era
Venue: arXiv cs.LG 2026
Year: 2026

Full Waveform Inversion (FWI) is a highly nonlinear and ill-posed problem that aims to recover subsurface velocity maps from surface-recorded seismic waveforms data. Existing data-driven FWI typically uses small models, as available datasets have limited volume, geological diversity, and spatial extent, leading to substantial concerns about overfitting. Although they perform well on synthetic datasets, current methods fail to generalize to more realistic geological structures. In this work, we show that a model trained entirely on simulated and relatively simple data can generalize remarkably well to challenging and unseen geological benchmarks. We provide a working recipe that tames a billion-parameter model for FWI through coordinated scaling across three axes: model capacity, data diversity, and training strategy. Our model achieves state-of-the-art performance on OpenFWI and significantly narrows the generalization gap in data-driven FWI. Across six challenging geophysical benchmarks, including Marmousi, 2D SEG/EAGE Salt and Overthrust, 2004 BP, Sigsbee, and SEAM Phase I, it infers complex structures absent from the training set and delivers significant performance improvements (SSIM from 0.5844 to 0.7669). Overall, our results demonstrate that with an appropriate scaling strategy, large models trained on simple synthetic data can achieve substantial generalization to more complex and realistic geological structures.
OBASE: Object-Based Address-Space Engineering to Improve Memory Tiering
Venue: arXiv cs.OS 2026
Year: 2026

Hardware and OS mechanisms for memory tiering are widely deployed, yet datacenters still overprovision DRAM. The root cause is hotness fragmentation: allocators place objects by size rather than access pattern, so hot and cold objects become interleaved within the same pages. A single hot object marks its page as active, trapping surrounding cold data in expensive DRAM. Our analysis of Google production workloads shows that up to 97% of the bytes in active pages are cold and unreclaimable. We propose address-space engineering: dynamically reorganizing virtual memory so that hot objects cluster into uniformly hot pages and cold objects into uniformly cold pages. We present OBASE, a compiler-runtime system for unmanaged languages that serves as an object-aware frontend for page-aware OS backends. OBASE tracks accesses via lightweight pointer instrumentation and migrates objects at runtime using a lock-free protocol that is safe under concurrency. By reorganizing the address space, OBASE enables unmodified backends (kswapd, TMO, TPP, Memtis) to tier memory effectively. Across ten concurrent data structures, six backends, and production traces from Meta and Twitter, OBASE improves page utilization by 2-4x and reduces memory footprint by up to 70%, with only 2-5% overhead.
Verifier-Bound Communication for LLM Agents: Certified Bounds on Covert Signaling
Venue: arXiv cs.CR 2026
Year: 2026

Colluding language-model agents can hide coordination in messages that remain policy-compliant at the surface level. We present CLBC, a protocol where generation and admission are separated: a message is admitted to transcript state only if a small verifier accepts a proof-bound envelope under a pinned predicate $Π$. The predicate binds policy hash, public randomness schedule, transcript chaining, latent schema constraints, canonical metadata/tool fields, and deterministic rejection codes. We show how this protocol yields an upper bound on transcript leakage in terms of latent leakage plus explicit residual channels, derive adaptive composition guarantees, and state a semantic lower bound when policy-valid alternatives remain choosable. We report extensive empirically grounded evidence: aggregate evaluation satisfies all prespecified thresholds; strict lane decoder advantage is bounded at 0.0000 with MI proxy 0.0636; adaptive-colluder stress tests remain below attacker thresholds; and baseline separation shows large gaps between reject-by-default semantics and audit-only controls. We further quantify operational tradeoffs. Strict full-proof mode has median turn latency 27.53s (p95 28.08s), while sampled proving reduces non-proved-turn latency to 0.327ms. The central finding is that bottlenecks alone are insufficient: security claims depend on verifiable admission semantics that are online, deterministic, and fail-closed.
Privacy Cards for Surfacing Mental Models and Exploring Privacy Concerns: A Case Study of Voice-First Ambient Interfaces with Older Adults
Venue: arXiv cs.HC 2026
Year: 2026

We investigate the ethical and privacy implications of voice-first ambient interfaces (VFAIs) for aging in place through an in-depth engagement with five older adults. Our participants were in the process of becoming experienced VFAI users, and had used a VFAI-based design probe for health data reporting. We create and iteratively refine an interview protocol using Privacy Cards. We customize Privacy Cards by drawing on participants' previous interviews and device usage logs. Using Privacy Cards, we conduct interviews to surface their mental models, and explore their privacy concerns. We find insufficient mental models for proper consent. For example, participants did not know who could access their data, and experienced difficulty distinguishing built-in functionality from third-party apps. Participants initially expressed little worry about VFAI-related ethical concerns, but interviews with Privacy Cards revealed nuanced issues, resulting in various implications for future research and design.
DiffTrans: Differentiable Geometry-Materials Decomposition for Reconstructing Transparent Objects
Venue: arXiv cs.CV 2026
Year: 2026

Reconstructing transparent objects from a set of multi-view images is a challenging task due to the complicated nature and indeterminate behavior of light propagation. Typical methods are primarily tailored to specific scenarios, such as objects following a uniform topology, exhibiting ideal transparency and surface specular reflections, or with only surface materials, which substantially constrains their practical applicability in real-world settings. In this work, we propose a differentiable rendering framework for transparent objects, dubbed DiffTrans, which allows for efficient decomposition and reconstruction of the geometry and materials of transparent objects, thereby reconstructing transparent objects accurately in intricate scenes with diverse topology and complex texture. Specifically, we first utilize FlexiCubes with dilation and smoothness regularization as the iso-surface representation to reconstruct an initial geometry efficiently from the multi-view object silhouette. Meanwhile, we employ the environment light radiance field to recover the environment of the scene. Then we devise a recursive differentiable ray tracer to further optimize the geometry, index of refraction and absorption rate simultaneously in a unified and end-to-end manner, leading to high-quality reconstruction of transparent objects in intricate scenes. A prominent advantage of the designed ray tracer is that it can be implemented in CUDA, enabling a significantly reduced computational cost. Extensive experiments on multiple benchmarks demonstrate the superior reconstruction performance of our DiffTrans compared with other methods, especially in intricate scenes involving transparent objects with diverse topology and complex texture. The code is available at https://github.com/lcp29/DiffTrans.
Weight Updates as Activation Shifts: A Principled Framework for Steering
Venue: arXiv cs.LG 2026
Year: 2026

Activation steering promises to be an extremely parameter-efficient form of adaptation, but its effectiveness depends on critical design choices -- such as intervention location and parameterization -- that currently rely on empirical heuristics rather than a principled foundation. We establish a first-order equivalence between activation-space interventions and weight-space updates, deriving the conditions under which activation steering can replicate fine-tuning behavior. This equivalence yields a principled framework for steering design and identifies the post-block output as a theoretically-backed and highly expressive intervention site. We further explain why certain intervention locations outperform others and show that weight updates and activation updates play distinct, complementary functional roles. This analysis motivates a new approach -- joint adaptation -- that trains in both spaces simultaneously. Our post-block steering achieves accuracy within 0.2%-0.9%$ of full-parameter tuning, on average across tasks and models, while training only 0.04% of model parameters. It consistently outperforms prior activation steering methods such as ReFT and PEFT approaches including LoRA, while using significantly fewer parameters. Finally, we show that joint adaptation often surpasses the performance ceilings of weight and activation updates in isolation, introducing a new paradigm for efficient model adaptation.
Semijoins of Annotated Relations
Venue: arXiv cs.DB 2026
Year: 2026

The semijoin operation is a fundamental operation of relational algebra that has been extensively used in query processing. Furthermore, semijoins have been used to formulate desirable properties of acyclic schemas; in particular, a schema is acyclic if and only if it has a full reducer, i.e., a sequence of semijoins that converts a given collection of relations to a globally consistent collection of relations. In recent years, the study of acyclicity has been extended to annotated relations, where the annotations are values from some positive commutative monoid. So far, however, it has not been known if the characterization of acyclicity in terms of full reducers extends to annotated relations. Here, we develop a theory of semijoins of annotated relations. To this effect, we first introduce the notion of a semijoin function on a monoid and then characterize the positive commutative monoids for which a semijoin function exists. After this, we introduce the notion of a full reducer for a schema on a monoid and show that the following is true for every positive commutative monoid that has the inner consistency property: a schema is acyclic if and only if it has a full reducer on that monoid.
Texterial: A Text-as-Material Interaction Paradigm for LLM-Mediated Writing
Venue: arXiv cs.HC 2026
Year: 2026

What if text could be sculpted and refined like clay -- or cultivated and pruned like a plant? Texterial reimagines text as a material that users can grow, sculpt, and transform. Current generative-AI models enable rich text operations, yet rigid, linear interfaces often mask such capabilities. We explore how the text-as-material metaphor can reveal AI-enabled operations, reshape the writing process, and foster compelling user experiences. A formative study shows that users readily reason with text-as-material, informing a conceptual framework that explains how material metaphors shift mental models and bridge gulfs of envisioning, execution, and evaluation in LLM-mediated writing. We present the design and evaluation of two technical probes: Text as Clay, where users refine text through gestural sculpting, and Text as Plants, where ideas grow serendipitously over time. This work expands the design space of writing tools by treating text as a living, malleable medium.
Rooted Absorbed Prefix Trajectory Balance with Submodular Replay for GFlowNet Training
Venue: arXiv cs.LG 2026
Year: 2026

Generative Flow Networks (GFlowNets) enable fine-tuning large language models to approximate reward-proportional posteriors, but they remain prone to mode collapse, manifesting as prefix collapse and length bias. We attribute this to two factors: (i) weak credit assignment to early prefixes, and (ii) biased replay that induces a shifted, non-representative training flow distribution. We propose Rooted absorbed prefix Trajectory Balance RapTB, an objective that anchors subtrajectory supervision at the root and propagates terminal rewards to intermediate prefixes via absorbed suffix-based backups, providing dense prefix-level learning signals. To mitigate replay-induced distribution shift, we further introduce SubM, a submodular replay refresh strategy that promotes both high reward and diversity. Empirically, on tasks such as molecule generation with LLM using SMILES strings, RapTB combined with SubM consistently improves optimization performance and molecular diversity while preserving high validity.
A Blockchain-Based Trust Framework for Resilient Cross-Domain UAV Service Orchestration
Venue: arXiv cs.CR 2026
Year: 2026

Unmanned aerial vehicle (UAV) networks are increasingly deployed for complex missions, including disaster response, intelligent logistics, and environmental monitoring. These missions generally require coordinated collaboration among multiple UAVs across distinct administrative domains. To support such cross-domain cooperation, service function chains (SFCs) are constructed, where complex workflows are decomposed into ordered service functions assigned to appropriate UAVs along the mission path. However, it is challenging to ensure secure, trustworthy, and low-latency cross-domain SFC orchestration in identity management, authentication, and resilience to node failures. To address these issues, this paper proposes a consortium blockchain-based trust architecture for cross-domain decentralized identity verification, auditable task execution, and dynamic service-aware orchestrator selection. The framework employs a hierarchical four-phase cross-domain authentication protocol covering the credential pre-verification, intra-domain execution, secure relay, and audit logging. The use case analysis confirms that the proposed framework achieves substantial reductions in authentication latency and significant improvements in system throughput against centralized and static schemes. The open challenges in scalability, adaptive trust assessment, interoperability, and energy efficiency are discussed, thereby providing directions for future researches on secure and efficient cross-domain UAV service orchestration.