OpenRaiser/Intern-Atlas huggingface dataset:
Once4All: Skeleton-Guided SMT Solver Fuzzing with LLM-Synthesized Generators
Venue: arXiv cs.SE 2026
Year: 2026

Satisfiability Modulo Theory (SMT) solvers are foundational to modern systems and programming languages research, providing the foundation for tasks like symbolic execution and automated verification. Because these solvers sit on the critical path, their correctness is essential, and high-quality test formulas are key to uncovering bugs. However, while prior testing techniques performed well on earlier solver versions, they struggle to keep pace with rapidly evolving features. Recent approaches based on Large Language Models (LLMs) show promise in exploring advanced solver capabilities, but two obstacles remain: nearly half of the generated formulas are syntactically invalid, and iterative interactions with LLMs introduce substantial computational overhead. In this study, we present Once4All, a novel LLM-assisted fuzzing framework that addresses both issues by shifting from direct formula generation to the synthesis of generators for reusable terms (i.e., logical expressions). Specifically, Once4All uses LLMs to (1) automatically extract context-free grammars (CFGs) for SMT theories, including solver-specific extensions, from documentation, and (2) synthesize composable Boolean term generators that adhere to these grammars. During fuzzing, Once4All populates structural skeletons derived from existing formulas with the terms iteratively produced by the LLM-synthesized generators. This design ensures syntactic validity while promoting semantic diversity. Notably, Once4All requires only one-time LLM interaction investment, dramatically reducing runtime cost. We evaluated Once4All on two leading SMT solvers: Z3 and cvc5. Our experiments show that Once4All has identified 43 confirmed bugs, 40 of which have already been fixed by developers.
Attacks on Approximate Caches in Text-to-Image Diffusion Models
Venue: arXiv cs.CR 2026
Year: 2026

Diffusion models are a powerful class of generative models that produce images and other content from user prompts, but they are computationally intensive. To mitigate this cost, recent academic and industry work has adopted approximate caching, which reuses intermediate states from similar prompts in a cache. While efficient, this optimization introduces new security risks by breaking isolation among users. This paper provides a comprehensive assessment of the security vulnerabilities introduced by approximate caching. First, we demonstrate a remote covert channel established with the approximate cache, where a sender injects prompts with special keywords into the cache system and a receiver can recover that even after days, to exchange information. Second, we introduce a prompt stealing attack using the approximate cache, where an attacker can recover existing cached prompts from hits. Finally, we introduce a poisoning attack that embeds the attacker's logos into the previously stolen prompt, leading to unexpected logo rendering for the requests that hit the poisoned cache prompts. These attacks are all performed remotely through the serving system, demonstrating severe security vulnerabilities in approximate caching. The code for this work is available.
Towards Mitigating Excessive Forgetting in LLM Unlearning via Entanglement-Guidance with Proxy Constraint
Venue: arXiv cs.LG 2026
Year: 2026

Large language models (LLMs) are trained on massive datasets that may include private or copyrighted content. Due to growing privacy and ownership concerns, data owners may request the removal of their data from trained models. Machine unlearning provides a practical solution by removing the influence of specific data without full retraining. However, most existing methods still suffer from over-unlearning due to the lack of a principled mechanism to regulate the forgetting boundary, leading to unnecessary utility degradation and heightened privacy and robustness risks. In this work, we propose EGUP (Entanglement-Guided Unlearning with Proxy Constraint), a novel framework that leverages entanglement and proxy constraint to guide the unlearning process while mitigating over-unlearning. Within each iteration, EGUP employs inter-sample entanglement to adaptively reweight the unlearning strength, assigning greater unlearning efforts to forget samples that are semantically closer to retained knowledge. Across iterations, EGUP leverages intra-sample entanglement to track the representation shift of each forget sample and dynamically adjust its unlearning effort. In addition, we incorporate a proxy constraint that approximates the model's expected outputs after unlearning, forming a reference boundary that softly regularizes the unlearning process. EGUP is compatible with existing gradient-based objectives and serves as a plug-and-play enhancement. We evaluate EGUP on the TOFU and MUSE benchmarks, demonstrating consistent improvements in the unlearning-utility trade-off across multiple LLMs. Moreover, EGUP achieves performance close to the retrained model while remaining scalable and robust.
AI Agentic Vulnerability Injection And Transformation with Optimized Reasoning
Venue: arXiv cs.CR 2026
Year: 2026

The increasing complexity of software systems and the sophistication of cyber-attacks have underscored the need for reliable automated software vulnerability detection. Data-driven approaches using deep learning models show promise but critically depend on the availability of large, accurately labeled datasets. Yet existing datasets either suffer from noisy labels, limited vulnerability coverage, or fail to reflect vulnerabilities as they occur in real-world software. This also limits large-scale benchmarking of such solutions. Automated vulnerability injection provides a way to address these limitations, but existing techniques remain limited in coverage, contextual fidelity, or injection success. In this paper, we present AVIATOR, the first AI-agentic vulnerability injection framework. AVIATOR decomposes vulnerability injection into a coordinated workflow of specialized AI agents, tool-based analysis, and iterative self-correction, explicitly mirroring expert reasoning. It integrates RAG and lightweight LoRA-based fine-tuning to produce realistic, category-specific vulnerabilities without relying on handcrafted patterns. Across three benchmarks, AVIATOR achieves high injection fidelity (91-95%) surpassing existing injection techniques in both accuracy and vulnerability coverage. When used for data augmentation to train deep learning-based vulnerability detection (DLVD) models, AVIATOR provides the strongest downstream gains in vulnerability detection. Across models and base datasets, AVIATOR improves average F1 scores by +22% over no augmentation, +25% over VGX, holding the prior best injection success rate, and +3% over VulScribeR, the prior state-of-the-art LLM-based injection model, with +7% higher recall and no precision loss. Its augmented data exhibits the lowest distributional distortion and scales efficiently with <2% syntax rejection at 4.3x lower cost than VulScribeR.
On the Theoretical Limitations of Embedding-Based Retrieval
Venue: arXiv cs.IR 2026
Year: 2026

Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning, instruction-following, coding, and more. These new benchmarks push embeddings to work for any query and any notion of relevance that could be given. While prior works have pointed out theoretical limitations of vector embeddings, there is a common assumption that these difficulties are exclusively due to unrealistic queries, and those that are not can be overcome with better training data and larger models. In this work, we demonstrate that we may encounter these theoretical limitations in realistic settings with extremely simple queries. We connect known results in learning theory, showing that the number of top-k subsets of documents capable of being returned as the result of some query is limited by the dimension of the embedding. We empirically show that this holds true even if we directly optimize on the test set with free parameterized embeddings. Using free embeddings, we then demonstrate that returning all pairs of documents requires a relatively high dimension. We then create a realistic dataset called LIMIT that stress tests embedding models based on these theoretical results, and observe that even state-of-the-art models fail on this dataset despite the simple nature of the task. Our work shows the limits of embedding models under the existing single vector paradigm and calls for future research to develop new techniques that can resolve this fundamental limitation.
Analysis of Semantic Communication for Logic-based Hypothesis Deduction
Venue: arXiv cs.IT 2026
Year: 2026

This work presents an analysis of semantic communication in the context of First-Order Logic (FOL)-based deduction. Specifically, the receiver holds a set of hypotheses about the State of the World (SotW), while the transmitter has incomplete evidence about the true SotW but lacks access to the ground truth. The transmitter aims to communicate limited information to help the receiver identify the hypothesis most consistent with true SotW. We formulate the objective as approximating the posterior distribution of the transmitter at the receiver. Using Stirling's approximation, this reduces to a constrained, finite-horizon resource allocation problem. Applying the Karush-Kuhn-Tucker conditions yields a truncated water-filling solution. Despite the problem's non-convexity, symmetry and permutation invariance ensure global optimality. Based on this, we design message selection strategies, both for single- and multi- round communication, and model the receiver's inference as an $m$-ary Bayesian hypothesis testing problem. Under the Maximum A Posteriori (MAP) rule, our communication strategy achieves optimal performance within budget constraints. We further analyze convergence rates and validate the theoretical findings through experiments, demonstrating reduced error over random selection and prior methods.
Universal Properties of Activation Sparsity in Modern Large Language Models
Venue: arXiv cs.LG 2026
Year: 2026

Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and interpretability. However, methods relying on exact zero activations do not directly apply to modern Large Language Models (LLMs), leading to fragmented, model-specific strategies for LLM activation sparsity and a gap in its general understanding. In this work, we introduce a general framework for evaluating sparsity robustness in contemporary LLMs and conduct a systematic investigation of this phenomenon in their feedforward~(FFN) layers. Our results uncover universal properties of activation sparsity across diverse model families and scales. Importantly, we observe that the potential for effective activation sparsity grows with model size, highlighting its increasing relevance as models scale. Furthermore, we present the first study of activation sparsity in diffusion-based LLMs. Overall, our work provides a comprehensive perspective and practical guidance for harnessing activation sparsity in LLM design and acceleration.
How to Compute a Moving Sum
Venue: arXiv cs.DS 2026
Year: 2026

Windowed recurrences are sliding window calculations where a function is applied iteratively across the window of data, and are ubiquitous throughout the natural, social, and computational sciences. In this monograph we explore the computational aspects of these calculations, including sequential and parallel computation, and develop the theory underlying the algorithms and their applicability. We introduce an efficient new sequential algorithm with low latency, and develop new techniques to derive and analyze the complexity and domain of validity of existing sequential algorithms. For parallel computation we derive new parallel and vector algorithms by relating windowed recurrences to the algebraic construction of semidirect products, and to algorithms for exponentiation in semigroups. In the middle chapters of the monograph we further develop the theory of semi-associativity and the algebraic conditions for representing function composition and function application by data. This systematizes the techniques used by practitioners to parallelize recurrence calculations. We end the monograph with an extensive gallery of examples of interest to specialists in many fields. Throughout the monograph new algorithms are described with pseudo-code transcribed from functioning source code.
Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents
Venue: arXiv cs.AI 2026
Year: 2026

We introduce the Block Rearrangement Problem (BRaP), a challenging component of large warehouse management which involves rearranging storage blocks within dense grids to achieve a goal state. We formally define the BRaP as a graph search problem. Building on intuitions from sliding puzzle problems, we propose five search-based solution algorithms, leveraging joint configuration space search, classical planning, multi-agent pathfinding, and expert heuristics. We evaluate the five approaches empirically for plan quality and scalability. Despite the exponential relation between search space size and block number, our methods demonstrate efficiency in creating rearrangement plans for deeply buried blocks in up to 80x80 grids.
Causal Consistency Regularization: Training Verifiably Sensitive Reasoning in Large Language Models
Venue: arXiv cs.AI 2026
Year: 2026

Large language models can produce correct answers while relying on flawed reasoning traces, partly because common training objectives reward final-answer correctness rather than faithful intermediate reasoning. This undermines trustworthiness in high-stakes settings. We propose Counterfactual Sensitivity Regularization (CSR), a training paradigm that improves reasoning faithfulness by enforcing causal consistency between reasoning steps and outcomes. CSR automatically applies operator-level interventions to reasoning traces, such as swapping "+" with "-", to generate minimally perturbed counterfactual rationales, and penalizes the model when these logically invalid traces still lead to the original answer. Our implementation is efficient, adding about 9 percent training overhead via a warm-start curriculum and token-subset optimization. We evaluate faithfulness using Counterfactual Outcome Sensitivity (COS), which measures how appropriately answers change under logical perturbations. Across arithmetic (GSM8K), logical deduction (ProofWriter), multi-hop question answering (HotpotQA), and code generation (MBPP), CSR yields improved accuracy versus faithfulness trade-offs, establishing a new Pareto frontier. CSR improves faithfulness over standard fine-tuning and process supervision by up to 70 percentage points, and transfers across model families with 94.2 to 96.7 percent success in structured domains. CSR also complements inference-time methods such as self-consistency. Overall, CSR offers a practical route to more reliable reasoning in structured domains, including mathematics, formal logic, and code, where operators are well-defined and verifiable, covering an estimated 40 to 60 percent of high-stakes reasoning deployments.
EigenBench: A Comparative Behavioral Measure of Value Alignment
Venue: arXiv cs.AI 2026
Year: 2026

Aligning AI with human values is a pressing unsolved problem. To address the lack of quantitative metrics for value alignment, we propose EigenBench: a black-box method for comparatively benchmarking language models' values. Given an ensemble of models, a constitution describing a value system, and a dataset of scenarios, our method returns a vector of scores quantifying each model's alignment to the given constitution. To produce these scores, each model judges the outputs of other models across many scenarios, and these judgments are aggregated with EigenTrust (Kamvar et al., 2003), yielding scores that reflect a weighted consensus judgment of the whole ensemble. EigenBench uses no ground truth labels, as it is designed to quantify subjective traits for which reasonable judges may disagree on the correct label. Hence, to validate our method, we collect human judgments on the same ensemble of models and show that EigenBench's judgments align closely with those of human evaluators. We further demonstrate that EigenBench can recover model rankings on the GPQA benchmark without access to objective labels, supporting its viability as a framework for evaluating subjective values for which no ground truths exist. The code is available at https://github.com/jchang153/EigenBench.
Maximum entropy temporal networks
Venue: arXiv cs.SI 2026
Year: 2026

Temporal networks consist of timestamped directed interactions that may appear continuously in time, yet few studies have directly tackled the continuous-time modeling of networks. Here, we introduce a maximum-entropy approach to temporal networks and with basic assumptions on constraints, the corresponding network ensembles admit a modular and interpretable representation: a set of global time processes and a static maximum-entropy edge, e.g. node pair, probability. This time- edge labels factorization yields closed-form log-likelihoods, degree, clustering and motif expectations, and yields a whole class of effective generative models. We provide the maximum-entropy derivation for the non-homogeneous Poisson Process (NHPP) intensities governing the probability of directed edges in temporal networks via the functional optimization over path entropy, connecting NHPP modeling to maximum-entropy network ensembles. NHPPs consistently improve log-likelihood over generic Poisson processes, while the maximum-entropy edge labels recover strength constraints and reproduce expected unique-degree curves. We discuss the limitations of this framework and how it can be integrated with multivariate Hawkes calibration procedures, renewal theory, and neural kernel estimation in graph neural networks.
Batch Query Processing and Optimization for Agentic Workflows
Venue: arXiv cs.DB 2026
Year: 2026

Large Language Models (LLMs) in agentic workflows combine multi-step reasoning, heterogeneous tool use, and collaboration across multiple specialized agents. Existing LLM serving engines optimize individual calls in isolation, while multi-agent frameworks focus on orchestration without system-level performance planning. As a result, repeated prompts, overlapping contexts, and fragmented CPU-GPU execution create substantial redundancy and poor hardware utilization, especially in batch analytics scenarios. We introduce Halo, a system that brings batch query processing and optimization into agentic LLM workflows. Halo represents each workflow as a structured query plan DAG and constructs a consolidated graph for batched queries that exposes shared computation. Guided by a cost model that jointly considers heterogeneous resource constraints, prefill and decode costs, cache reuse, and GPU placement, Halo performs plan-level optimization to minimize redundant execution. The Processor integrates adaptive batching, KV-cache sharing and migration, along with fine-grained CPU-GPU pipelining to maximize holistic hardware efficiency. Evaluation across six benchmarks shows that Halo achieves up to 3.6x speedup for batch inference and 2.6x throughput improvement under online serving, scaling to workloads of thousands of queries and complex graphs. These gains are achieved without compromising output quality. By unifying query optimization with heterogeneous LLM serving, Halo enables efficient agentic workflows in data analytics and decision-making applications.
A Novel Framework for Uncertainty-Driven Adaptive Exploration
Venue: arXiv cs.AI 2026
Year: 2026

Adaptive exploration methods propose ways to learn complex policies via alternating between exploration and exploitation. An important question for such methods is to determine the appropriate moment to switch between exploration and exploitation and vice versa. This is critical in domains that require the learning of long and complex sequences of actions. In this work, we present a generic adaptive exploration framework that employs uncertainty to address this important issue in a principled manner. Our framework includes previous adaptive exploration approaches as special cases. Moreover, we can incorporate in our framework any uncertainty-measuring mechanism of choice, for instance mechanisms used in intrinsic motivation or epistemic uncertainty-based exploration methods. We experimentally demonstrate that our framework gives rise to adaptive exploration strategies that outperform standard ones across several environments.
Equivariant Flow Matching for Symmetry-Breaking Bifurcation Problems
Venue: arXiv cs.LG 2026
Year: 2026

Bifurcation phenomena in nonlinear dynamical systems often lead to multiple coexisting stable solutions, particularly in the presence of symmetry breaking. Deterministic machine learning models are unable to capture this multiplicity, averaging over solutions and failing to represent lower-symmetry outcomes. In this work, we formalize the use of generative AI, specifically flow matching, as a principled way to model the full probability distribution over bifurcation outcomes. Our approach builds on existing techniques by combining flow matching with equivariant architectures and an optimal-transport-based coupling mechanism. We generalize equivariant flow matching to a symmetric coupling strategy that aligns predicted and target outputs under group actions, allowing accurate learning in equivariant settings. We validate our approach on a range of systems, from simple conceptual systems to physical problems such as buckling beams and the Allen--Cahn equation. The results demonstrate that the approach accurately captures multimodal distributions and symmetry-breaking bifurcations. Moreover, our results demonstrate that flow matching significantly outperforms non-probabilistic and variational methods. This offers a principled and scalable solution for modeling multistability in high-dimensional systems.
TRACE: Unlocking Effective CXL Bandwidth via Lossless Compression and Precision Scaling
Venue: arXiv cs.AR 2026
Year: 2026

LLM inference is increasingly limited by memory bandwidth, and the bottleneck worsens at long context as the KV cache grows. CXL memory adds capacity to offload weights and KV, but its link and device-side DDR bandwidth are far below HBM, so decoding stalls once traffic shifts to the CXL tier. Many CXL controllers are starting to add generic \emph{lossless} compression, yet applying commodity codecs directly to standard word-major LLM tensors is largely ineffective, especially for token-major KV streams. We propose TRACE (\textbf{T}raffic-\textbf{R}educed \textbf{A}rchitecture for \textbf{C}ompression and \textbf{E}lasticity), which preserves the unmodified CXL.mem interface but changes the device-internal representation. It stores tensors in a channel-major, disaggregated bit-plane layout, and applies a KV-specific transform before compression, converting mixed-field words into low-entropy plane streams that commodity codecs can compress. The same substrate enables precision-proportional fetch by reading only the required bit-planes. Across public LLMs, TRACE reduces BF16 weight footprint by 25.2\% and BF16 KV footprint by 46.9\% losslessly, with per-layer KV ratios peaking at 2.69$\times$. In trace-driven system modeling, once KV spills to CXL, GPT-OSS-120B-MXFP4 improves throughput at 128k tokens from 16.28 to 68.99 tok/s (4.24$\times$). DRAMSim3 shows up to 40.3\% lower DRAM access energy under plane-aligned fetch. A 7\,nm SystemVerilog implementation sustains 256\,GB/s device bandwidth. Relative to a CXL controller with generic inline lossless compression, TRACE only adds 7.2\% area, 4.7\% power, and 6.0\% load-to-use latency at 2\,GHz and 0.7\,V.