AI News Archive: July 8, 2026 — Part 15

Sourced from 500+ daily AI sources, scored by relevance.

• Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data

  Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness. Differential Privacy (DP) has become a gold standard for privacy-preserving data analysis, while fairness-aware mechanisms aim to mitigate discrimination against underrepresent...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07471v1
• SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation

  Fine-tuning large language models (LLMs) for e-commerce attribute extraction requires labeled data representative across thousands of product types, attributes, and multiple languages. This combinatorial scale translates to millions of annotations, rendering human labeling prohibitively costly. Whil...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07469v1
• SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis

  Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics. As the fundamental paradigm for reconstructing cell developmental paths, trajectory inference (TI) is critical. However, existing methods require extensive manual intervention and proficiency in heterogeneou...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07467v1
• Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents

  Tool-using LLM agents can violate the very policies they are deployed to enforce while appearing to complete the task successfully. In policy-permissive environments, a tool may execute any well-formed call even when the corresponding state transition is forbidden by domain policy. The result is a s...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07405v1
• Heterogeneity-Adaptive Diffusion Schrodinger Bridge for PET-Guided Whole-Body MRI Translation

  While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acquisition time in PET-MR scanning is a major obstacle in more efficient clinical practice. Deep learning-based MRI translation provides a potential sol...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07401v1
• When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs

  Reliable confidence estimation remains a key limitation of test-time adaptation in vision-language models (VLMs), where prompt tuning improves zero-shot accuracy but often degrades calibration due to entropy-driven overconfidence. Prior approaches mitigate this using LLM-derived class attributes and...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07395v1
• Physics-Audited Agentic Discovery in Scientific Machine Learning

  In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, typically an error metric. A low error, however, does not establish that the predicted fields satisfy the physics that matter for mechanics, such as b...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07379v1
• On Adversarial Vulnerability of Vision-Language Models through the Lens of Intermediate Spectral Subspaces

  Adversarial vulnerability in deep neural networks (DNNs) has been studied from the perspectives of decision-boundary geometry, feature robustness, input-output Jacobians, and the instability of inverse problems. Here, we focus on the spectral structure of intermediate linear transformations that pro...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07375v1
• Hypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation

  Hypergraph neural networks have shown powerful capability in modeling higher-order relations, yet their predictive uncertainty remains underexplored. Unlike pairwise graphs, uncertainty in hypergraphs arises not only from noisy attributes and ambiguous labels, but also from variations in node-hypere...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07330v1
• From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents

  Tool utilization enables Large Language Model (LLM) agents to interact with the real world and resolve complex tasks. However, existing agent frameworks predominantly rely on static toolsets composed of granular atomic actions (e.g., basic file I/O or single-turn search), which forces agents to rein...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07321v1
• FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

  Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself. Recently, centralized Taking Away Training Data (TATD) attacks have shown that malicious training could abuse the memorization ca...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07314v1
• Multimodal Voice Activity Projection for Turn-Taking in Social Robots with Voice-Activity-Related Pretrained Encoders

  Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot must anticipate conversational dynamics rather than merely react to pauses. This work presents a Multimodal Voice Activity Projection (MM-VAP) framewo...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07294v1
• Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

  Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through sc...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07708v1
• Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass

  Analytical workloads operating on data stored in external database systems face a fundamental bottleneck: data access is guarded entirely by the database driver, like JDBC or ODBC, forcing all reads through query execution and other driver layers that are not designed for bulk columnar analytics. We...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07696v1
• SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents

  Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but correct, secure, and maintainable. We introduce SkillCenter, to our knowledge the largest open skill library for agents by to...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07676v1
• DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation

  Large language models increasingly \emph{understand} dialectal English, yet still \emph{produce} only standard, US-leaning English, leaving dialectal generation, the harder half of the problem, largely unaddressed. We introduce \textbf{DiaLLM}, which continually pretrains three open-weight language ...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07669v1
• Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops

  AI systems increasingly participate in their own improvement: revising their outputs, adapting their own harnesses during deployment, training on data they generate, and, increasingly, conducting AI research itself. This literature is described under a vocabulary ("self-refine," "self-reward," "self...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07663v1
• QCNN with Rough Path Signature Kernels

  Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges. A major difficulty arises from the time reparameterization invariance of time series data, which complicates the extraction of meaningful temporal feature...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07634v1
• Creativity from Friction: Human-AI Interaction for Exploratory Structural Design

  AI agents that generate final answers based on user input often do not meet the needs of creative fields. Fields such as structural design and architecture need interactive systems that help users externalise and develop ideas, explore alternatives, and refine partial solutions. The final product of...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07521v1
• Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26

  Testing is a major effort for the gaming industry, requiring a significant part of development budget and people power. We present a case study on a development version of the ice hockey game EA SPORTS NHL 26, for which human playtesters test the goalie AI for behavioral exploits. To reduce the effo...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07498v1
• Search, Fail, Recover: A Training Framework for Correction-Aware Reasoning

  Many reasoning tasks are not well described by a single left-to-right chain: a solver may need to pursue a plausible branch, observe delayed failure, and return to the latest prefix that can still be completed. We introduce Pyligent, a training and inference framework inspired by the Diligent Learne...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07492v1
• Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents

  Agentic red-teaming benchmarks report whether an injected agent was compromised as a single bit: the attack succeeded, or it did not. We argue that this binary attack-success rate discards the information a defender most needs, namely how harmful the resulting action was. We introduce an action-grad...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07474v1
• The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents

  A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural constraint that keeps a growing library from drifting below the no-skill baseline, but its guarantee assumes an unbiased reward, which is fal...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07436v1
• RLVP: Penalize the Path, Reward the Outcome

  Agents acting on our behalf in the real world (e.g. placing phone calls) must learn online from costly, often irreversible interactions rather than cheap simulator steps. Two things follow. First, deployability depends on the path, not only the outcome. An agent must respect outcome-neutral constrai...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07435v1
• InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs

  Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption. Current works mainly focus on Existential First Order Logic (EFO) queries. These EFO queries contain conjunction, disjunction, and negation operators. Most existing ...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07422v1
• Agentic Data Environments

  Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs. The central challenge for agentic automation is therefore to increase the benefits of automation while bounding the consequences of failure. While d...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07397v1
• MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning

  Mathematical reasoning benchmarks typically provide all facts needed to solve each problem, while interactive benchmarks often mix reasoning with tools, retrieval, and long-horizon dialogue. We introduce MIRA-Math, a benchmark for a narrower diagnostic capability: solving mathematical problems whose...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07391v1
• Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors

  AI control is a family of techniques to prevent an AI with malicious goals from subverting its operator's intent. AI Control usually studies a single agent in one trajectory, but real deployments run many agents over shared infrastructure, and the most severe risks (model-weight exfiltration, traini...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07368v1
• HumAIN: Human-Aware Implicit Social Robot Navigation

  Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skeletal cues like gait and orientation. We present Human-Aware Implicit Social Robot Navigation (HumAIN), a novel framework that fuses implicit social cues directly into the planning loop via kno...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07357v1
• HAJJv2-CrowdCount: Zero-Shot Benchmark for Dense Crowd Counting

  Automated crowd counting in Hajj video is difficult not because current models lack capacity, but because the footage violates the assumptions those models were built on: cameras observe the crowd from steep, near-vertical angles, individuals occlude one another extensively, and a single frame can c...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07322v1
• CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training

  Accurately estimating urban carbon emissions is critical for sustainable urban planning, yet many existing approaches remain difficult to apply consistently across cities due to data-source heterogeneity and the lack of fine-grained semantic-temporal context in remote sensing data. We propose Carbon...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07292v1
• From Noisy Traces to Root Causes: Structural Trajectory Analysis and Causal Extraction for Agent Optimization

  The optimization of long-horizon agents increasingly relies on reflection-based mechanisms, where a large language model (LLM) acts as an optimizer to diagnose agent failures and improve agent policies. However, real execution traces are difficult to use directly for optimization: large trace collec...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07702v1
• PALS: Percentile-Aware Layerwise Sparsity for LLM Pruning

  One-shot pruning methods like Wanda and SparseGPT apply the same sparsity ratio to every layer of a transformer, ignoring known variation in layer importance. We propose PALS (Percentile-Aware Layerwise Sparsity), which adjusts per-layer sparsity based on the 99th percentile of activation magnitudes...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07557v1
• Think Big, Search Small: Where Capacity Matters in Hierarchical Search Agents?

  Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queries and dispatches them to parallel sub-agents. However, existing systems instantiate all roles from a single model of identical scale, leaving open ...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07548v1
• DeLS-Spec: Decoupled Long-Short Contexts for Parallel Speculative Drafting

  Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel. Block-parallel drafters such as DFlash further improve drafting efficiency by predicting an entire block in one pass, but their position-wise predictions lack explicit intra-block causal condit...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07409v1
• Transformer-based segmentation of prosodic boundaries in Brazilian Portuguese

  Automatic prosodic segmentation identifies boundaries between speech units from acoustic and linguistic evidence. Although recent deep learning approaches have produced strong results for English, automatic segmentation for Brazilian Portuguese (BP) still relies mostly on rule-based or traditional m...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07408v1
• TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models

  Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion costly through pretraining, fine-tuning, retrieval augmentation, or longer contexts. Engram-style memory offers a compact hidden-state injection pa...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07388v1
• Riemannian Geometry for Pre-trained Language Model Embeddings

  Understanding the geometric structure of pre-trained language model embeddings matters for interpretability and safety. We ask whether sentence-level classification signal lives in the Riemannian geometry of contextual token embeddings, and probe it by extracting per-token pullback metrics from a le...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07047v1
• Dissociating the Internal Representations of Sycophancy in LLMs

  Large Language Models (LLMs) frequently exhibit sycophancy, where they agree with a user's statement even when incorrect. While sycophancy is often treated as a single defined behavior, it can manifest in substantially distinct ways and circumstances, raising the question of whether this multi-facet...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07003v1
• MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning

  Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in isolation. When problems arrive sequentially, accumulating reusable experience across them can further improve performance. Existing memory-based m...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.06974v1
• Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies

  Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks. T...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.06963v1
• Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System

  The remarkable performance of large language models (LLMs) in linguistic tasks underscores an urgent need for comprehensive evaluation of their response quality. Prevailing methods, often confined to singular dimensions, fall short of capturing the full spectrum of model capabilities. This study int...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.06940v1
• Max Out GRPO Signal: Adaptive Trace Prefix Control for Hard Reasoning Problems

  Group Relative Policy Optimization (GRPO) stalls on a model's hardest problems: when no rollout in a group succeeds, the group-relative advantages vanish and the problem contributes no gradient, wasting the frontier examples we most want to learn from. Prepending a correct prefix of a reference solu...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07674v1
• Does Bielik Know What It Doesn't Know? Activation Dispersion Separates Entity Familiarity from Factual Reliability Across Model Scale

  Large language models hallucinate most about entities they have never seen. We ask whether a model's activations betray entity familiarity before a single answer token is generated, and whether that signal predicts the factual reliability of the answers. On four Polish Bielik models (1.5B-11B parame...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07670v1
• FourierQK: Spectral Preprocessing of Query-Key Projections Improves Transformer Attention

  FFT-based spectral preprocessing of learned query-key (Q/K) projections substantially improves transformer attention on character-level language modelling. On TinyShakespeare: a fixed random spectral filter achieves val=1.031 (Delta=+0.443); a single learned frequency at paragraph scale achieves val...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07478v1
• R^3: Advertisement Compliance Rectification via Group-Relative Experience Extractor and Curriculum Reinforcement

  Rigorous content moderation is crucial for online advertising but leads to millions of daily rejections. This scale renders manual rectification infeasible, particularly for video advertisements. However, existing safety-driven methods often suffer from aggressive over-editing, which compromises the...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07318v1
• Evaluating RAG Metrics in Applied Contexts: An Experiment, Its Findings and Its Limitations

  This paper reports an empirical study evaluating the relevance of several RAG metrics. The experiment is based on a question-answering dataset created by human annotators from business data. The generated responses and retrieved spans of a RAG system are scored using evaluation metrics from four lib...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07302v1
• Orus

  Claude for investing in perpetuals

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• Evaluation of Multilingual Ability to Use Spatial Deictic Expressions in Vision-Language Models

  One of the expected abilities of vision-language models (VLMs) is spatial reasoning ability based on a given text and image. To evaluate the spatial reasoning abilities of VLMs, we focus on the use of spatial deictic expressions, which are defined as spatial expressions whose referent is determined ...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07251v1
• Behavior Leverage Imbalance in Multi-Teacher On-Policy Distillation

  Agentic language models must learn when to call tools, when to consume tool responses, and when to answer directly. This makes multi-teacher on-policy distillation a natural training strategy: one teacher can specialize in tool calls, another in direct responses, and the student can learn from both ...

  📄 ResearchJul 8, 2026http://arxiv.org/abs/2607.07050v1