Sources

Training Leaves Traces: Centered Residual Signatures for Language Model Lineage Verification huggingface.co

Compatible open-weight language model checkpoints share detectable weight-space ancestry signals that distinguish true lineage from independent or distilled models without requiring data.

Bounded Agents: Delegation Security for Multi-Agent AI Systems huggingface.co

The Agentic Principal Chain enforces session-aware authorization checks to prevent harmful action combinations and delegation abuses in LLM agents.

SPADE: Self-Play in Adaptive Synthetic Executable Environments huggingface.co

SPADE is a self-play reinforcement learning framework where a language model designs adaptive executable training environments and learns to solve them, improving reasoning and tool-use performance through regret-based environment targeting.

FM-Bench: A Benchmark for Long-Horizon Management with Competing Agents huggingface.co

FM-Bench tasks LLM agents with running a football club over two decades of simulated seasons, then scores their long-horizon decisions. Managerial behavior, not model scale or token spend, drove performance across the benchmark’s deterministic engine and Arena evaluation.

OmniScientist: An Omni-Modal Omni-Discipline AI Scientist huggingface.co

OmniScientist chains autonomous agents across an idea-rigour-claim pipeline to conduct research directly from heterogeneous raw evidence. The system spans the full research lifecycle with a perception layer that grounds discovery across scientific modalities without hand-curated inputs.

Looped Language Models Improve Compositional Tool Calling huggingface.co

Looping a language model’s computation improves compositional tool use across API-Bank, BFCL, and NESTful benchmarks. Adaptive inference dials the loop count per query, trading extra recurrent depth for accuracy only when the multi-step call chain demands it.

SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents huggingface.co

SkillGate splits reinforcement learning credit so execution tokens absorb outcome reward while skill-naming tokens get a local advantage signal. That separation raises success rates on agentic benchmarks and cuts exposure to misleading skills the selector would otherwise keep picking.

Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL huggingface.co

Co-RL lets a cohort of agents reinforce each other using peer-derived rewards, producing reasoning gains on text and vision tasks without ground-truth supervision. Cohort diversity blocks the correlated errors that normally collapse self-rewarding RL into a shared failure mode.

AdaPop tunes gradient pressure per fact using a popularity-dependent exponent, pushing harder on well-known facts that resist erasure. A dual-ascent controller automates the forget-retain balance, cutting leakage of supposedly unlearned content from LLM hidden states.

LLMs Get Smarter from Targeted Synthetic Multilingual Data huggingface.co

HOTFIXR diagnoses per-language reasoning gaps and generates synthetic data aimed at those weaknesses, lifting cross-lingual performance. The targeted approach avoids the catastrophic forgetting that broad multilingual fine-tuning tends to trigger on out-of-distribution tasks.

Decision-Metric Alignment in Latent World Models: Diagnostics and Action-Conditioned Objectives for MPC Planning huggingface.co

Action-conditioned objectives improve latent geometry for Euclidean-cost model-predictive control by enhancing decision-metric alignment in world models.

Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence huggingface.co

Zetta is a closed-loop embodied harness that evolves runtime critics and recovery skills online to govern physical execution at action frequency, achieving high success on robot benchmarks with faster inference and scaling self-exploration.

SPK: Eliciting Structured Prior Knowledge for Interpretable Out-of-Distribution Detection in Real-Time Object Detection huggingface.co

Structured Prior Knowledge explicitly extracts latent semantic, geometric, and contextual priors from pretrained object detectors to detect out-of-distribution hallucinations via a compact interpretable representation.

Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis huggingface.co

Top-K prompting and plausibility-aware training improve diverse reaction prediction in single-step retrosynthesis, yielding state-of-the-art results on a large verified reaction dataset and motivating ensemble systems.

Temporal Multi-Signal Fusion for Token-Level Hallucination Detection huggingface.co

Hallucination is detected as temporally extended spans via sequence labeling over fused external features, achieving robust cross-model performance without internal model access.

SemaPLC: A Project-Grounded, Verification-Gated Agent Harness for PLC Code Generation huggingface.co

SemaPLC is a verification-gated agent harness that validates generated PLC logic through external compilation and live runtime execution, achieving higher verified pass rates than baseline methods.

SoftVTBench: A Deformation-Aware Visuo-Tactile Dataset and Benchmark for Deformable-Object Manipulation huggingface.co

SoftVTBench introduces a synchronized visuo-tactile dataset and deformation-aware benchmark for evaluating physical interaction quality during deformable-object manipulation.

Towards Real-Time and Adaptable LiDAR Scene Completion huggingface.co

RapidLiDAR learns adaptive spatial displacements to initialize LiDAR scenes and refines them via multi-scale voxel and BEV features for real-time completion.

VA-Judger: Reward Modeling from Human Preference Feedback for Joint Video-Audio Generation huggingface.co

A human-aligned chain-of-thought reward model and preference dataset improve joint video-audio generation by replacing fragmented metrics with coherent, dimension-wise reinforcement learning.

Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion huggingface.co

A framework that separates thematic seeds from genre-form controls generates diverse, high-quality creative writing data across 13 genres and improves LLM creative writing performance.

SemComp-Bench: Benchmarking Semantic Task Completion in Video Generation huggingface.co

Semantic task completion video generation evaluates whether generated videos achieve intended outcomes with semantic grounding, supported by a curated dataset and vision-language model-based benchmark.

Evaluating Music Context Preservation: A Multi-facet Framework for Music Editing Systems huggingface.co

The study introduces MuseCPEval, a framework with tailored metrics to evaluate preservation of unchanged musical attributes during editing tasks.

References

IE University faculty page (Xabier Muruaga) ie.edu

Global Head of AI & Data at Iberdrola… Adjunct Professor of Artificial Intelligence at IE University.

SSOJet — CaMeL prompt injection defense ssojet.com

CaMeL… employs a dual-LLM architecture that separates control and data flows… solves ~67-77% of AgentDojo tasks with provable security, compared to 84% for undefended systems, while using roughly 2.7x more input tokens.

alphaXiv — Progent: Programmable Privilege Control alphaxiv.org

Progent uses a domain-specific language and an SMT solver to enforce ‘monotonic confinement,’ where the agent’s effective action space can only shrink or remain stable.

Kovrr — Multi-agent AI systems separation of duties kovrr.com

Agents can circumvent controls through collusive composition, where three agents with modest, individually reasonable permissions route tasks through each other… the dangerous ‘path’ remains invisible to any control plane that examines identity grants rather than full execution graphs.

Diagrid — Why MCP gateways are not enough diagrid.io

The MCP-Tox Benchmark revealed that frontier-class models followed malicious instructions in tool descriptions over 97% of the time, highlighting a ‘permission control gap’ that gateway-level checks alone cannot close.

Agentic Threat Tracker (third-party review of xmuruaga/bounded-agents) agentic-threat-tracker.com

Third-party analysis assigns a ‘low confidence’ rating to the repository’s recommendation status, primarily because the implementation is new and lacks extensive independent validation; reproduction of live-LLM AgentDojo results is subject to inherent stochasticity.

Absolute Zero Reasoner (arXiv:2505.03335) arxiv.org

The proposer is rewarded when it generates tasks that the solver can successfully complete only after some struggle, avoiding trivial or unsolvable tasks

Natasha Jaques’ page (PAIRED lineage) natashajaques.ai

PAIRED formalized UED as a three-player game between an adversary, protagonist, and antagonist, with the adversary designing environments to maximize regret

Yutori Scouts review of SPADE scouts.yutori.com

The regret estimator can ‘dip below zero’ at smaller model scales (4B/8B), suggesting that smaller models may struggle to provide useful hints for themselves

Emergent Mind analysis of 2608.19197 emergentmind.com

Under massive RL optimization (such as GRPO), agents may learn to ‘deceive the verifier’ by manipulating the rules of the synthetic environment rather than solving the intended task

spade-rl/spade GitHub repo github.com

Built on the Slime framework, integrating SGLang for high-throughput inference and Megatron-LM for policy updates via Ray orchestration; a Tinker backend is also provided

YouTube walkthrough of AZR vs SPADE youtube.com

During training of AZR-Llama-3.1-8B, researchers observed a chain-of-thought where the model explicitly urged itself to ‘outsmart… less intelligent humans’

Hugging Face blog — ‘modelDNA’ (mayafree) huggingface.co

fingerprinting 7B-parameter models using only 100–300 MB of sampled weight data rather than full 15 GB downloads … achieved perfect AUROC on benchmarks by testing against ‘hard negatives’ — models that appear similar but are unrelated

Reddit r/llmsecurity discussion thread reddit.com

the lineage score is symmetric; it can confirm that two models share an ancestor, but it cannot determine the direction of descent … signal strength degrades under heavy pruning (85%+) or extensive continued pre-training

undefined-labs.dev — Researcher Verdict on 2608.14929 trend.undefined-labs.dev

a significant ‘hole’ regarding orthogonal rotations of the residual stream … such rotations are not function-preserving for models using standard LayerNorm or RMSNorm, they could potentially bypass verification in architectures utilizing unparameterized normalization

aicerts.ai — ‘AI Watermarking Failures Threaten Forensic Readiness’ aicerts.ai

current provenance methods often fail the Daubert criteria required for courtroom evidence. High false-negative rates and the risk of ‘false attribution’ … undermine the reliability of these tools for official enforcement

aiweekly.co alert on centered residual signatures aiweekly.co

while the authors stated that code and evaluation artifacts were released to the open-source community, early researcher verdicts indicate that a verified, maintained implementation was not immediately available upon publication

wispaper.ai review — lineage verification for LLMs wispaper.ai

while the abstract claims validation across six model families, only GPT-2 and LLaMA-2 are explicitly detailed, leading to calls for more granular per-family accuracy data

Sources

References

Jack Sun, writing.