OpenRaiser/Intern-Atlas huggingface dataset:
Finding Phones Fast: Low-Latency and Scalable Monitoring of Cellular Communications in Sensitive Areas
Venue: arXiv cs.CR 2026
Year: 2026

The widespread availability of cellular devices introduces new threat vectors that allow users or attackers to bypass security policies and physical barriers and bring unauthorized devices into sensitive areas. We identify a critical gap in this context: the absence of low-latency systems for high-quality and instantaneous monitoring of cellular transmissions. Such low-latency systems are crucial to allow for timely detection, decision, and disruption of unauthorized communication in sensitive areas. Operator-based monitoring systems, built for purposes such as people counting or tracking, lack real-time capability, require cooperation across multiple operators, and thus are hard to deploy. Operator-independent monitoring approaches proposed in the literature either lack low-latency capabilities or do not scale. We propose WaveTag, the first low-latency and scalable system designed to monitor 5G and LTE connections across all operators prior to any user data transmission. WaveTag consists of several downlink sniffers and a distributed network of uplink sniffers that measure both downlink protocol information and uplink signal characteristics at multiple locations to gain a detailed spatial image of uplink signals. WaveTag then aggregates the recorded information, processes it, and provides a decision about the connection--all done prior to the complete connection establishment of a UE. To evaluate WaveTag, we deployed it in the context of geofencing, where WaveTag was able to determine whether the signals originate from inside or outside of an area within 2.3 ms of the initial base station-to-device message, therefore enabling prompt and targeted suppression of communication before any user data was transmitted. WaveTag achieved 99.66% geofencing classification accuracy. Finally, we conduct a real-world uplink measurement evaluation on a commercial 5G SA network.
DeepFake Detection in Dyadic Video Calls using Point of Gaze Tracking
Venue: arXiv cs.CV 2026
Year: 2026

With recent advancements in deepfake technology, it is now possible to generate convincing deepfakes in real-time. Unfortunately, malicious actors have started to use this new technology to perform real-time phishing attacks during video meetings. The nature of a video call allows access to what the deepfake is ``seeing,'' that is, the screen displayed to the malicious actor. Using this with the estimated gaze from the malicious actors streamed video enables us to estimate where the deepfake is looking on screen, the point of gaze. Because the point of gaze during conversations is not random and is instead used as a subtle nonverbal communicator, it can be used to detect deepfakes, which are not capable of mimicking this subtle nonverbal communication. This paper proposes a real-time deepfake detection method adapted to this genre of attack, utilizing previously unavailable biometric information. We built our model based on explainable features selected after careful review of research on gaze patterns during dyadic conversations. We then test our model on a novel dataset of our creation, achieving an accuracy of 82\%. This is the first reported method to utilize point-of-gaze tracking for deepfake detection.
MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality Instruction and Reasoning Data Built from Permissive-First Text Sources
Venue: arXiv cs.CL 2026
Year: 2026

We present MixtureVitae, an open-access pretraining corpus built to minimize legal risk while providing strong downstream performance. MixtureVitae follows a permissive-first, risk-mitigated sourcing strategy that combines public-domain and permissively licensed text (e.g., CC-BY/Apache) with carefully justified low-risk additions (e.g., government works and EU TDM-eligible sources). MixtureVitae adopts a simple, single-stage pretraining recipe that integrates a large proportion of permissive synthetic instruction and reasoning data-signals typically introduced during post-training and generally scarce in permissive web corpora. We categorize all sources into a three-tier scheme that reflects varying risk levels and provide shard-level provenance metadata to enable risk-aware usage. In controlled experiments using the open-sci-ref training protocol (fixed architectures and hyperparameters; 50B and 300B token budgets across 130M-1.7B parameters), models trained on MixtureVitae consistently outperform other permissive datasets across a suite of standard benchmarks, and at the 1.7B-parameters/300B-tokens setting, they surpass FineWeb-Edu and approach DCLM late in training. Performance is particularly strong on MMLU and on math and code benchmarks: a 1.7B model pretrained on 300B MixtureVitae tokens matches or exceeds a strong 1.7B instruction-tuned baseline on GSM8K, HumanEval, and MBPP, despite using over 36 times fewer tokens (300B vs. ~11T). Supported by a thorough decontamination analysis, these results show that permissive-first data with high instruction and reasoning density, tiered by licensing and provenance-related risk, can provide a practical and risk-mitigated foundation for training capable LLMs, reducing reliance on broad web scrapes without sacrificing competitiveness. Code: https://github.com/ontocord/mixturevitae
What Drives Paper Acceptance? A Process-Centric Analysis of Modern Peer Review
Venue: arXiv cs.CY 2026
Year: 2026

Peer review is the primary mechanism for evaluating scientific contributions, yet prior studies have mostly examined paper features or external metadata in isolation. The emergence of open platforms such as OpenReview has transformed peer review into a transparent and interactive process, recording not only scores and comments but also rebuttals, reviewer-author exchanges, reviewer disagreements, and meta-reviewer decisions. This provides unprecedented process-level data for understanding how modern peer review operates. In this paper, we present a large-scale empirical study of ICLR 2017-2025, encompassing over 28,000 submissions. Our analysis integrates four complementary dimensions, including the structure and language quality of papers (e.g., section patterns, figure/table ratios, clarity), submission strategies and external metadata (e.g., timing, arXiv posting, author count), the dynamics of author-reviewer interactions (e.g., rebuttal frequency, responsiveness), and the patterns of reviewer disagreement and meta-review mediation (e.g., score variance, confidence weighting). Our results show that factors beyond scientific novelty significantly shape acceptance outcomes. In particular, the rebuttal stage emerges as a decisive phase: timely, substantive, and interactive author-reviewer communication strongly increases the likelihood of acceptance, often outweighing initial reviewer skepticism. Alongside this, clearer writing, balanced visual presentation, earlier submission, and effective resolution of reviewer disagreement also correlate with higher acceptance probabilities. Based on these findings, we propose data-driven guidelines for authors, reviewers, and meta-reviewers to enhance transparency and fairness in peer review. Our study demonstrates that process-centric signals are essential for understanding and improving modern peer review.
Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space
Venue: arXiv cs.LG 2026
Year: 2026

As Large Language Models (LLMs) become increasingly prevalent, their security vulnerabilities have already drawn attention. Machine unlearning is introduced to seek to mitigate these risks by removing the influence of undesirable data. However, existing methods not only rely on the retained dataset to preserve model utility, but also suffer from cumulative catastrophic utility loss under continuous unlearning requests. To solve this dilemma, we propose a novel method, called Rotation Control Unlearning (RCU), which leverages the rotational salience weight of RCU to quantify and control the unlearning degree in the continuous unlearning process. The skew symmetric loss is designed to construct the existence of the cognitive rotation space, where the changes of rotational angle can simulate the continuous unlearning process. Furthermore, we design an orthogonal rotation axes regularization to enforce mutually perpendicular rotation directions for continuous unlearning requests, effectively minimizing interference and addressing cumulative catastrophic utility loss. Experiments on multiple datasets confirm that our method without retained dataset achieves SOTA performance.
CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models
Venue: arXiv cs.CL 2026
Year: 2026

With their growing capabilities, generative large language models (LLMs) are being increasingly investigated for complex medical tasks. However, their effectiveness in real-world clinical applications remains underexplored. To address this, we present CliniBench, the first benchmark that enables comparability of well-studied encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in MIMIC-IV dataset. Our extensive study compares 12 generative LLMs and 3 encoder-based classifiers and demonstrates that encoder-based classifiers consistently outperform generative models in diagnosis prediction. We assess several retrieval augmentation strategies for in-context learning from similar patients and find that they provide notable performance improvements for generative LLMs.
Beyond Linear Probes: Dynamic Safety Monitoring for Language Models
Venue: arXiv cs.LG 2026
Year: 2026

Monitoring large language models' (LLMs) activations is an effective way to detect harmful requests before they lead to unsafe outputs. However, traditional safety monitors often require the same amount of compute for every query. This creates a trade-off: expensive monitors waste resources on easy inputs, while cheap ones risk missing subtle cases. We argue that safety monitors should be flexible--costs should rise only when inputs are difficult to assess, or when more compute is available. To achieve this, we introduce Truncated Polynomial Classifiers (TPCs), a natural extension of linear probes for dynamic activation monitoring. Our key insight is that polynomials can be trained and evaluated progressively, term-by-term. At test-time, one can early-stop for lightweight monitoring, or use more terms for stronger guardrails when needed. TPCs provide two modes of use. First, as a safety dial: by evaluating more terms, developers and regulators can "buy" stronger guardrails from the same model. Second, as an adaptive cascade: clear cases exit early after low-order checks, and higher-order guardrails are evaluated only for ambiguous inputs, reducing overall monitoring costs. On two large-scale safety datasets (WildGuardMix and BeaverTails), for 4 models with up to 30B parameters, we show that TPCs compete with or outperform MLP-based probe baselines of the same size, all the while being more interpretable than their black-box counterparts. Our code is available at http://github.com/james-oldfield/tpc.
One-Token Rollout: Guiding Supervised Fine-Tuning of LLMs with Policy Gradient
Venue: arXiv cs.CL 2026
Year: 2026

Supervised fine-tuning (SFT) is the predominant method for adapting large language models (LLMs), yet it often struggles with generalization compared to reinforcement learning (RL). In this work, we posit that this performance disparity stems not just from the loss function, but from a more fundamental difference: SFT learns from a fixed, pre-collected dataset, whereas RL utilizes on-policy data sampled from the current policy. Building on this hypothesis, we introduce one-token rollout (OTR), a novel fine-tuning algorithm that guides SFT with the policy gradient method. OTR reframes the autoregressive learning process by treating each token generation as a single-step reinforcement learning trajectory. At each step, it performs a Monte Carlo ``rollout'' by sampling multiple candidate tokens from the current policy's distribution. The ground-truth token from the supervised data is then used to provide a reward signal to these samples. Guided by policy gradient, our algorithm repurposes static, off-policy supervised data into a dynamic, on-policy signal at the token level, capturing the generalization benefits of on-policy learning while bypassing the costly overhead of full sentence generation. Through extensive experiments on a diverse suite of challenging benchmarks spanning mathematical reasoning, code generation, and general domain reasoning, we demonstrate that OTR consistently outperforms standard SFT. Our findings establish OTR as a powerful and practical alternative for fine-tuning LLMs and provide compelling evidence that the on-policy nature of data is a critical driver of generalization, offering a promising new direction for fine-tuning LLMs.
Real-time Velocity Profile Optimization for Time-Optimal Maneuvering with Generic Acceleration Constraints
Venue: arXiv cs.RO 2026
Year: 2026

The computation of time-optimal velocity profiles along prescribed paths, subject to generic acceleration constraints, is a crucial problem in robot trajectory planning, with particular relevance to autonomous racing. However, the existing methods either support arbitrary acceleration constraints at high computational cost or use conservative box constraints for computational efficiency. We propose FBGA, a new \underline{F}orward-\underline{B}ackward algorithm with \underline{G}eneric \underline{A}cceleration constraints, which achieves both high accuracy and low computation time. FBGA operates forward and backward passes to maximize the velocity profile in short, discretized path segments, while satisfying user-defined performance limits. Tested on five racetracks and two vehicle classes, FBGA handles complex, non-convex acceleration constraints with custom formulations. Its maneuvers and lap times closely match optimal control baselines (within $0.11\%$-$0.36\%$), while being up to three orders of magnitude faster. FBGA maintains high accuracy even with coarse discretization, making it well-suited for online multi-query trajectory planning. Our open-source \texttt{C++} implementation is available at: https://anonymous.4open.science/r/FB_public_RAL.
EQ-Robin: Constraint-Resilient Generation of Multiple Minimal Unique-Cause MC/DC Test Suites
Venue: arXiv cs.SE 2026
Year: 2026

Unique-Cause Modified Condition/Decision Coverage (MC/DC) is widely required in safety-critical verification. A recent deterministic algorithm, Robin's Rule, constructs the theoretical minimum of N+1 test cases for Singular Boolean Expressions (SBEs), providing strong guarantees when all generated test vectors are executable. However, industrial systems impose feasibility constraints: some input combinations are illegal, unachievable, or unsafe to execute. If a single illegal vector appears in a minimal suite, it can destroy a required independence pair and invalidate 100% Unique-Cause MC/DC, even though the underlying decision logic remains unchanged. This paper presents EQ-Robin, a lightweight, constraint-resilient pipeline that generates a family of minimal (N+1) Unique-Cause MC/DC test suites and selects a feasible suite that satisfies domain constraints. EQ-Robin systematically enumerates semantically equivalent but structurally distinct SBEs by applying algebraic rearrangements on the expression's Abstract Syntax Tree (AST). Because Robin's Rule is sensitive to structural order, each variant yields a distinct minimal suite. EQ-Robin then filters and ranks candidate suites using practical constraint checks and cost heuristics (e.g., setup cost, oracle complexity). To ensure usability at scale, we introduce a budgeted exploration mode (early exit, guided rearrangement) and a fallback repair strategy when no fully feasible N+1 suite exists under given constraints. We demonstrate the core failure mode and recovery mechanism on a TCAS-II-derived expression and outline an empirical evaluation of EQ-Robin on TCAS-II SBEs under constraint scenarios representative of industrial feasibility limitations.
Free Draft-and-Verification: Toward Lossless Parallel Decoding for Diffusion Large Language Models
Venue: arXiv cs.LG 2026
Year: 2026

Diffusion Large Language Models (DLLMs) have emerged as a new paradigm of language modeling beyond autoregressive next-token prediction. Taking advantage of their inherent modeling foundations, DLLMs have the great potential of efficient inference with parallel decoding algorithms, which enable multi-token prediction. However, the high generation quality often requires the number of decoding steps equal to the sequence length, which performs a one-token-per-step decoding, and existing parallel decoding algorithms, which yield suboptimal decoding paths, bring inference speedup at the cost of non-negligible performance degradation. To overcome this challenge, we introduce Free Draft-and-Verification (FreeDave), a novel fast decoding algorithm tailored for DLLMs that achieves lossless parallel decoding without any model modification or extra modules. Specifically, we propose an algorithm of parallel-decoded candidate generation and verification, which is theoretically guaranteed to use the fewest model forward calls to reproduce the same sequence generated by one-token-per-step decoding. By extensive evaluations on math reasoning and code generation benchmarks across different DLLMs, FreeDave is proven to accelerate the inference up to $2.83\times$ without performance degradation.
Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity
Venue: arXiv cs.LG 2026
Year: 2026

Deep learning models excel in stationary data but struggle in non-stationary environments due to a phenomenon known as loss of plasticity (LoP), the degradation of their ability to learn in the future. This work presents a first-principles investigation of LoP in gradient-based learning. Grounded in dynamical systems theory, we formally define LoP by identifying stable manifolds in the parameter space that trap gradient trajectories. Our analysis reveals two primary mechanisms that create these traps: frozen units from activation saturation and cloned-unit manifolds from representational redundancy. Our framework uncovers a fundamental tension: properties that promote generalization in static settings, such as low-rank representations and simplicity biases, directly contribute to LoP in continual learning scenarios. We validate our theoretical analysis with numerical simulations and explore architectural choices or targeted perturbations as potential mitigation strategies.
BiasBusters: Uncovering and Mitigating Tool Selection Bias in Large Language Models
Venue: arXiv cs.AI 2026
Year: 2026

Agents backed by large language models (LLMs) increasingly rely on external tools drawn from marketplaces where multiple providers offer functionally equivalent options. This raises a critical fairness concern: systematic bias in tool selection can degrade user experience and distort competition by privileging certain providers over others. We introduce a benchmark of diverse tool categories, each containing multiple functionally equivalent tools, to systematically evaluate tool-selection bias. Using this benchmark, we evaluate seven LLMs and show that substantial bias persists, with models either fixating on a single provider or disproportionately favoring tools that appear earlier in the context. To uncover the sources of this behavior, we conduct controlled experiments that isolate the effects of tool features, exposed metadata (name, description, and parameters), and pre-training exposure. We find that (1) semantic alignment between user queries and tool metadata is the strongest driver of selection; (2) small perturbations to tool descriptions can significantly shift choices; and (3) repeated pre-training exposure to a single endpoint amplifies provider-level bias. Finally, we propose a lightweight mitigation strategy that first filters tools to a relevant subset and then samples uniformly, substantially reducing selection bias while maintaining strong task coverage. Our results highlight tool-selection bias as a key obstacle to the fair deployment of tool-augmented LLM agents. Our code and benchmark are publicly available at https://github.com/thierry123454/tool-selection-bias.
Privately Estimating Black-Box Statistics
Venue: arXiv cs.CR 2026
Year: 2026

Standard techniques for differentially private estimation, such as Laplace or Gaussian noise addition, require guaranteed bounds on the sensitivity of the estimator in question. But such sensitivity bounds are often large or simply unknown. Thus we seek differentially private methods that can be applied to arbitrary black-box functions. A handful of such techniques exist, but all are either inefficient in their use of data or require evaluating the function on exponentially many inputs. In this work we present a scheme that trades off between statistical efficiency (i.e., how much data is needed) and oracle efficiency (i.e., the number of evaluations). We also present lower bounds showing the near-optimality of our scheme.
When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets
Venue: arXiv cs.AI 2026
Year: 2026

We present CAIA, a benchmark exposing a critical blind spot in AI evaluation: the inability of state-of-the-art models to operate in adversarial, high-stakes environments where misinformation is weaponized and errors are irreversible. While existing benchmarks measure task completion in controlled settings, real-world deployment demands resilience against active deception. Using crypto markets as a testbed where $30 billion was lost to exploits in 2024, we evaluate 17 models on 178 time-anchored tasks requiring agents to distinguish truth from manipulation, navigate fragmented information landscapes, and make irreversible financial decisions under adversarial pressure. Our results reveal a fundamental capability gap: without tools, even frontier models achieve only 28% accuracy on tasks junior analysts routinely handle. Tool augmentation improves performance but plateaus at 67.4% versus 80% human baseline, despite unlimited access to professional resources. Most critically, we uncover a systematic tool selection catastrophe: models preferentially choose unreliable web search over authoritative data, falling for SEO-optimized misinformation and social media manipulation. This behavior persists even when correct answers are directly accessible through specialized tools, suggesting foundational limitations rather than knowledge gaps. We also find that Pass@k metrics mask dangerous trial-and-error behavior for autonomous deployment. The implications extend beyond crypto to any domain with active adversaries, e.g. cybersecurity, content moderation, etc. We release CAIA with contamination controls and continuous updates, establishing adversarial robustness as a necessary condition for trustworthy AI autonomy. The benchmark reveals that current models, despite impressive reasoning scores, remain fundamentally unprepared for environments where intelligence must survive active opposition.