AI safety covers the practices aimed at making AI systems reliable, aligned with human intentions, and free from harmful behaviors. Anthropic, for instance, builds Claude around explicit safety principles and alignment research.
Benchmark of 1,200 clinical documents with 9,184 uncertainty annotations across five levels. LLMs poorly preserve uncertainty expressions (less than 50% of cases) and struggle with nuanced distinctions between adjacent levels. Reveals a failure mode missed by standard metrics.
Local de-identification framework for educational dialogues. Two-stage cascade: union proposer (lightweight encoders + deterministic rules) generates PII candidates, then binary Redact/Keep reviewer uses dialogue context and speaker role. Achieves 0.958 macro F1 on math tutoring transcripts, outperforms commercial API (0.706) and local LLM baseline (0.767), runs on single laptop.
Study of collateral damage in LLM machine unlearning. Authors show damage propagates beyond the forget set following semantic distance gradients, and propose PreUnlearn, a pre-unlearning prediction method to audit risks before execution.
Memorization suppression method in LLMs via output vector editing of MLP neurons. Tested on 4 models (360M-7B parameters), achieves 87.9% suppression on OLMo-7B with 6831 memorized sequences. Complementary approach to existing neuron ablation methods.
RedactionBench is a manually annotated benchmark of 200 documents across 11 domains for evaluating PII redaction in context. Introduced with R-Score, a character-level metric, it shows 35 models (NER, SLM, frontier models) fail on contextual redactions: human consensus 89.4% for mandatory redactions, 47.7% for contextual ones.
Study on evaluating AI-generated radiology reports. Researchers show existing LLMs over-penalize harmless rephrasings while detecting clinical errors. They train lightweight metrics on Qwen3-8B and MedGemma-4B outperforming 32B medical models, with dataset and metric release planned.
Self-CTRL optimizes consistency between language models' self-explanations and behavior via reinforcement learning. On probabilistic reasoning tasks, the method improves R² correlation from 0.24 to 0.64. In constitutional AI, it increases refusal prediction from 36% to 92% and reduces HarmBench failure rate from 15.0% to 0.5%.
SciRisk-Bench is a safety evaluation benchmark for LLMs in AI4Science workflows. It covers 7 disciplines, 31 sub-disciplines, and 10 risk dimensions. The authors evaluate mainstream and science-oriented LLMs to diagnose safety gaps across risk categories.
Safety Reflection Pretraining inserts short safety reflections into pretraining corpora to establish self-monitoring directly in language modeling. On 1.7B models pretrained on FineWeb-Edu, the method improves safety classification accuracy and substantially reduces success rates of inference-stage and finetuning attacks.
PhysAssistBench is an interactive medical assistance benchmark with 1,296 physician-validated turns built from real MIMIC-IV cases. It evaluates LLMs' ability to coordinate clinical knowledge, patient communication, and EHR system interaction within single dialogues. Experiments show current models remain unreliable in this setting.
ImpSH, a triplet-based framework, improves implicit hate speech detection by aligning posts with implied statements and using context-bounded semi-hard negatives. Evaluated on IHC, SBIC, and DynaHate with BERT and HateBERT, it enhances cross-domain performance and provides more stable representations than standard supervised contrastive approaches.
Sparse Autoencoders (SAEs) decompose activations into interpretable features, but this study shows that clamping a 'harmful' feature does not eliminate the behavior—it can recover via other residual pathways. Even with active intervention, 95.8% behavior recovery is achievable in refusal-steering, exposing a gap between feature-level control and behavioral completeness.
Reject inference methods used in credit scoring to correct survival bias mask a structural failure: accuracy can improve while the ability to correctly reject defaulters collapses. Authors propose a controlled exploration strategy (approving 2-5% of rejected applicants) to diagnose this deterioration without strong statistical assumptions.
Causal framework for sleep recovery scoring from multimodal polysomnography. Uses DAG learning on two cohorts (MESA n=1540, MrOS n=825) to identify five physiological domains (respiratory burden, hypoxia, fragmentation, architecture, autonomic regulation). Sleep Recovery Score (SRS) achieves 2.5× stronger alignment with perceived recovery than standard AHI.
License plate cameras will soon track phones, wearables, infotainment systems and pets via Bluetooth and WiFi. Mass surveillance technology in development.
Security audit of 200 blockchain-related NPM packages: assessment of deprecation and hijack risks. Scoring methodology applied to critical dependency ecosystem.
Anthropic deploys a security expert to government officials to address AI safety concerns. The move aims to establish direct dialogue between the company and regulators on safety and alignment issues.
Poll: Only 16% of Americans believe AI will have a positive societal impact. Majority expresses concerns about economic and social effects, while experts remain more optimistic.
OpenAI researchers propose a method to predict how often a new AI model will make mistakes after release. This approach could fill gaps left by standard safety testing.
Article arguing for increased engineering discipline in AI development, against trends minimizing technical standards. Criticizes 'move fast and break things' approach applied to critical systems.
LLMs can infer cultural context but fail to apply it in responses. A new CAPRI dataset shows models recognize cultural conventions (measurement units, time interpretation) but don't spontaneously use them unless explicitly instructed. Biases remain aligned with the model's country of origin.
Theoretical paper on Sum-of-Squares degree barriers for robust halfspace learning under malicious noise. The Christoffel function exactly characterizes corruption hidden from bounded-degree certificates. Proves a margin-degree tradeoff and a degree-2t algorithm achieving the frontier η^(1-1/2t).
Researchers identify an internal signature of deception in language models: deceptive responses show 2.1-2.3x higher residual rank than naively false answers. This signature detects deception with 100% accuracy on GPT-2, Qwen2.5, and Phi-3, and transfers zero-shot across model families and languages (AUC 0.933-1.0).
AIPatient Arena evaluates LLMs in multi-turn clinical consultation across 8 competence dimensions using EHR-grounded knowledge graphs. On 437 patients, models excel in questioning (4.43-4.99/5) and ethical conduct (4.38-4.93/5), but fail in diagnostic accuracy (2.63-3.55/5) and information coverage (2.08-3.02/5). Weaknesses include repetitive questioning, omitted medical history, inadequate uncertainty handling.
Study of second-order bias in LLMs: how models judge biased content, beyond generation. Grounded in entitlement epistemology, the method evaluates whether LLMs infer demographics without sufficient support. Findings: systematic bias across target groups, evasion of safety guardrails, persistence of demographic triggers.
Study of 450 chest X-ray reports showing LLM rewriting for standardization preserves image-text alignment (2.5% degradation) but erodes 26.8–29.3% of clinical entities and 14.9–16.5% of uncertainty language. The paradox: tasks producing 'cleaner' text pull content away from images.
InferBERT combines transformers with Do-calculus to detect causal adverse drug events in pharmacovigilance. Comparative study on AILF and TRAM benchmarks: BioBERT outperforms XGBoost, ALBERT, and Med-LLaMA. Finding: domain-specific pre-training outweighs model size.
MM++ is an unsupervised, post-hoc method for out-of-distribution detection. It fuses intermediate layers selected by entropy density with the final representation using Ledoit-Wolf regularized covariance, requiring no auxiliary OOD data, fine-tuning, or architectural changes.
Comparative study of three recurrent architectures (LSTM, GRU, Mamba) and two algorithms (PPO, SAC) for meta-reinforcement learning applied to input-constrained control barrier functions (ICCBF) in spacecraft proximity operations. Mamba + PPO outperforms other setups in safety, task completion, and fuel savings across cooperative and adversarial scenarios.
MorphStrata enhances Moving Target Defense for time-series forecasting models via selective layer-specific stochastic noise injection. Tested on Transformer with FGSM, BIM and PGD attacks, the approach reduces adversarial RMSE by up to 97.97% on AEP data with training overhead <1%.
Hierarchical Bayesian credibility framework for pricing autonomous vehicle liability under operational design domain shifts. Tested on 648 verified Waymo crashes (4 US cities, 116M miles): credibility weights moderate (0.12-0.46), partial pooling decisively outperforms no pooling, learned kernel advantage detectable at ~12 deployed cities.
Clinical decision support AI system using Digital Twins, Treatment Effect estimation, and Reinforcement Learning for adaptive real-time treatment recommendations. Validated on synthetic simulator and TCGA ovarian cancer dataset. Safety module with rule-based vital sign monitoring and clinician escalation for high-uncertainty cases.
MODE-RAG is a multi-agent system driven by Variational Free Energy to reduce hallucinations in Multimodal Retrieval-Augmented Generation. It uses Monte Carlo Tree Search, logit perturbations, and specialized agents to route high-risk queries and perform post-hoc factual verification. Authors introduce ModeVent, a challenging subset of MultiVent dataset, to evaluate M-RAG robustness.
STATEWITNESS, an activation explainer, detects deception in reasoning LLMs by reading the target model's hidden states and answering natural-language queries. Achieves 0.916 mean AUROC, 11.6% relative gain over best black-box text monitor, 25.0% over best activation-probe baseline. Provides token- and sentence-level evidence traces for human inspection.
Systematic evaluation of foundation model representations (5 FMs) on computational pathology tasks using whole-slide images and transcriptomic profiles (IH-BC, IH-NSCLC cohorts). Multimodal fusion improves performance when no single modality dominates. Conformal prediction shows true diagnosis remains recoverable in prediction sets for majority of failed predictions.
CheckMIABench introduces a benchmark for principled evaluation of membership inference attacks (MIAs) on language models. Leveraging intermediate checkpoints from open-source models (Pythia, OLMo, 70M–7B parameters), the authors construct reliable testbeds where training data before and after a fixed point share the same distribution. They evaluate six published attacks and release a modular library (pandora_llm).
SpeechDx is a multi-task benchmark for clinical speech AI covering 12 datasets and 27 tasks across diverse health conditions. Tasks are structured by speech production stages (conceptualization, formulation, articulation). Evaluation of 12 audio encoders shows large-scale speech models outperform domain-specific ones, but none generalize reliably across clinical speech.
Foundation model-orchestrated workflow for pedestrian protection design. Integrates ML surrogate (R²=0.87), multi-objective evolutionary search, geometry generator, and LLM interface. Reduces evaluation time from hours to seconds; generates 35 safety-compliant alternatives in automotive bumper case study.
Researchers propose Equation-to-Behavior Prompting to guide LLMs to simulate diverse cognitive models (Bayesian, motivated reasoning, Grether's α-β model). Large models approximate these specifications via prompting, but small models fail. RL training reduces belief error by 26.5% and improves performance by 2.5–12% on legal persuasion games.
DiagFlowBench evaluates how language models handle off-procedure inputs in industrial diagnostic dialogue. A dataset of 1,676 multi-turn conversations derived from 50 diagnostic flowcharts reveals models often select a real but contextually inadequate step rather than hallucinate, exposing a vulnerability: plausible but wrong advice grounded in documentation.