Developer Tools B

An arXiv paper studies online scams as multi-stage lifecycles of temporally ordered rails and events, tracking scam trends and rail paths from Reddit self-disclosure narratives. It introduces an annotated dataset to address the lack of open resources covering scam-type relations. Neutral, abstract-based summary.

Federated learning enables collaborative medical image segmentation without centralizing sensitive data, but real-world deployment faces label imperfections like contour disagreement and confused labels. The authors argue existing federated noisy-label learning relies on synthetic noise and simplified settings, and introduce a benchmark suite combining diverse real-world noisy datasets, deployment-relevant client-noise scenarios, and label-noise-targeted evaluation to guide method selection.

An arXiv paper presents a systematic analysis of how LLMs can learn, via reinforcement learning, to adapt their query formulation strategies to different retrievers in retrieval-augmented generation. Summarized neutrally from the abstract.

Score-based models are increasingly used for wireless physical-layer tasks, but it is unclear when they beat discriminative learning. Using channel estimation as a case study, the paper interprets score-based estimation through the perception-distortion tradeoff, identifying when score matching excels and quantifying the excess risk of distortion-minimizing approaches.

An arXiv paper presents a semi-supervised framework that scales LLM reasoning from minimal supervision, using a lightweight reasoning-correctness classifier to turn verification into a data-creation mechanism. Summarized neutrally from the abstract.

The paper observes that large reasoning models can recognize safety risks when re-presented with the original query alongside their own reasoning trace—a property it calls latent safety awareness. To exploit this without heavy manual annotation, it uses supervised fine-tuning to induce safe tags that trigger safety analysis.

An arXiv paper presents an LLM-based visual programming copilot for aerospace geometric design tasks, using a visual-programming variant of the ReAct methodology. Summarized neutrally from the abstract; claims are the authors' and not independently verified.

A developer shares lessons from building an LLM-driven tool that answers user questions via a customer API. Capturing and writing down domain knowledge is much of the work, easier than earlier AI generations since it need not be rigidly structured, yet exactly where prior efforts foundered.

The paper presents MIXGUARD, a mixup-based privacy-preserving split-learning framework for LLMs combining token- and representation-level obfuscation with adaptive gradient perturbation to balance utility, privacy, and efficiency. Claims reflect the abstract.

Standard generative scenario models optimize uniform distributional fit rather than downstream decision quality. Decision-Weighted Flow Matching (DW-FM) reweights the velocity-regression objective using decision-sensitive endpoint information, linking downstream regret to pathwise velocity mismatch and providing regret-aligned objectives with guarantees.

Algorithmic explanations often need expert knowledge to read and can be uninformative about complex decision functions. The authors propose Explanation Cards that augment explanations with robustness and validity information plus clear interpretation instructions, making otherwise uninformative explanations practically useful while flagging when they are not.

The paper proposes Skill-to-LoRA (S2L), a behavior-centric representation that replaces runtime skill text—commonly distributed as SKILL.md files—with skill-specific LoRA adapters. Rather than compressing the document, S2L models the behavioral change the skill text induces, aiming at more token-efficient LLM agents.

The paper presents UNIATTACK, an adversarial testing framework that systematically builds effective black-box attack prompts on LLMs from a defense-oriented perspective. Unlike static templates or model-specific tuning, it extracts minimal but high-impact features from diverse existing attacks and optimizes them.

MyPCBench evaluates computer-use agents as personal assistants on a Linux desktop with 17 simulated web apps and 184 persona-seeded tasks, benchmarking six closed and open-weight models. Reported scores reflect the paper and are not independently verified.

The paper proposes detecting AI-generated videos, especially those from text-to-video models, by amplifying noise to reveal subtle artifacts that distinguish them from authentic footage. It notes that prior work largely targeted GAN-generated samples and frames text-to-video detection as still underexplored.

The paper separates current-affect estimation from future affective-change forecasting in longitudinal self-report text, proposing the TSAP/E-TSAP and ACF-Hybrid frameworks. It reports textual representations differ in usefulness across the two tasks; figures reflect the abstract.

The paper proposes a progressive, physics-guided multi-scale vibration-processing pipeline for bearing fault diagnosis, using a kinematics-derived descriptor for real-time screening and fault-adaptive segmentation. Reported figures reflect the abstract and are not independently verified.

Note: the abstract was unavailable, so this is summarized neutrally from the title alone. The paper appears to characterize LLM sycophancy as a 'material failure under pushback loading,' across three loading cases and up to seventeen 'material charges.' Specific methods and findings cannot be confirmed from the title.

This arXiv paper addresses tool selection for LLM agents whose harnesses connect to hundreds or thousands of APIs, where exhaustive tool-schema injection is costly and imposes a closed-world assumption. Noting that one-shot retrieval often fails to align isolated tool descriptions with the agent's true intent—especially in long-horizon tasks—the authors propose SING, a Synthetic Intention Graph for scalable, active tool discovery.

This arXiv paper proposes agentic automata learning to assess how well tool-calling LLM agents can uncover hidden environments through interaction. An agent must infer a hidden deterministic finite automaton (DFA) via membership and equivalence queries, yielding a scalable testbed with controlled task complexity. Evaluating state-of-the-art LLMs, the authors find performance drops sharply as DFA size grows, with reasoning models markedly stronger.

This arXiv paper studies multiparty turn-taking—essential for spoken dialogue systems but hard amid overlap and rapid speaker changes—on the VoxConverse dataset. The authors propose an audio-only two-stage pipeline that separates when to trigger a turn boundary from whether the floor actually transfers: a fast trigger proposes candidate end-of-turn times, and a lightweight verifier runs only at those times to decide Hold or Shift.

This arXiv paper introduces two complementary benchmark datasets for computational lexical semantic change (LSC) detection. The Bi-Directional Lexical Semantic Change (BD-LSC) dataset captures sense gain, loss, and stability across three time periods, targeting cases—especially slang versus standard usage—where words simultaneously gain and lose senses, which existing benchmarks struggle to capture.

While knowledge is managed via Wikipedia and software via GitHub, agent skills still lack infrastructure for large-scale production, governance, and evolution. SkillWiki is a living knowledge infrastructure turning heterogeneous knowledge into reusable skill assets linked to their originating evidence. It presents the full skill lifecycle, from knowledge ingestion to provenance-aware exploration, governance, and execution-driven evolution, with a live demo and source code available.

Arvind Narayanan and Sayash Kapoor examine AI-driven job loss through software engineering, a field unusually exposed to AI disruption. They argue the evidence rejects the narrative that AI will trigger mass layoffs once it crosses a capability threshold, and that more regulated, less exposed professions are likely even more cushioned.

A shared video essay frames AI, gods, and the self as 'incredibly effective illusions' — constructs that shape human experience despite lacking real solidity, drawing a parallel between AI and long-standing notions of deity and identity.

The Pyodide 314.0 release lets developers publish Python packages built for Pyodide, or any runtime compatible with the PyEmscripten platform defined in PEP 783, as WASM wheels on PyPI, a long-awaited step for browser-based Python.

An early 0.1a0 release of luau-wasm packages Luau, Roblox's typed Lua dialect, for WebAssembly, built using the newly enabled approach for publishing WASM wheels to PyPI for Pyodide.

A technical note exploring how to map columns in arbitrary SQLite query results back to their originating table.column, so that Datasette could render queries with richer, source-aware metadata.

A police officer is under investigation for allegedly using AI to 'create evidence' in multiple cases, raising concerns about the misuse of generative AI within the justice system.

An IEEE Journal Watch piece on using visual language models to let robots interpret human emotions. As robots gain dexterity and increasingly work alongside people, reading facial and emotional cues could make human-robot collaboration safer and smoother.