AI, Security & Engineering
James Trappett's research and projects span artificial intelligence, machine learning, cybersecurity, and scalable systems architecture. Below are the latest articles organised by research category.
A new human-LLM framework constructs EspanStereo, the first native Spanish stereotype dataset, revealing significant cross-country bias variation in LLMs.
Bias & FairnessMultilingual NLPDataset ConstructionLLM EvaluationA technical analysis of Mesh LLM, which pools heterogeneous GPUs across iroh endpoints to serve large model inference without centralised infrastructure.
Distributed SystemsLLM InferencePeer-to-PeerQUICA position paper argues that AI mathematics systems must shift from competition solvers to research agents, reviewing formal proof methods and frontier results.
AI ResearchMathematicsLLMsFormal VerificationReCoLoRA addresses catastrophic forgetting in sequential LLM fine-tuning using spectral adapter initialisation and recursive weight consolidation across tasks.
Continual LearningPEFTLoRALLM Fine-TuningDeepSearch-World offers a verifiable environment and self-distillation framework enabling web agents to improve from their own experience, reaching 61.5% on GAI
LLM AgentsReinforcement LearningWeb SearchSelf-ImprovementA new paper shows agentic decomposition on ARC-AGI-1 reaches 67.25% pass@2 at $0.62 per task using no benchmark-specific training or heavy compute.
ARC-AGIReasoningAgentsBenchmarksTriRoute unifies three transformer efficiency mechanisms into one learned controller, Pareto-dominating independent tuning at matched inference cost across 160M
EfficiencyTransformersMixture of ExpertsInferenceA new theoretical framework reveals when reflection-driven reasoning yields exponential gains over parallel sampling, and when it offers no benefit at all.
LLMsReasoningTheoryInferenceA unified benchmark comparing KV-cache quantization, pruning, and merging reveals compression ratio alone poorly predicts end-to-end LLM serving performance.
LLM ServingKV-CacheBenchmarkingInference EfficiencyA narratology-grounded memory system for AI fiction writing beats temporal knowledge graph baselines by wide margins on multi-hop story-state questions.
AI ResearchNatural Language ProcessingKnowledge GraphsCreative AIFLORA corrects a fundamental LLM inconsistency where models generate responses they then reject, improving generator AUROC by up to 7.3pp and G-V correlation by
LLMsNLPModel AlignmentResearch PaperGoogle DeepMind's Gemma 4 introduces encoder-free multimodal processing, thinking mode reasoning, and MoE architectures spanning 2.3B to 31B parameters.
LLMMultimodalOpen SourceEfficiencyA new paper exposes five pipeline failure modes in perturbation-based AI benchmark audits, showing how clean-looking numbers can mask broken evaluation pipeline
AI GovernanceBenchmarkingEvaluationSafetyA technical deep-dive into Riddle, a Rust application that turns the reMarkable Paper Pro into a Harry Potter-style AI diary using vision LLMs and e-ink.
Embedded AIE-InkRustLLMA critical analysis of Vessel, an EGA-style adventure game exploring whether machines can grieve, and what it reveals about AI affect modelling and narrative de
AI ResearchGame DesignMachine ConsciousnessAffect ComputingA critical analysis of the economic and legal arguments for compensating public contributors whose writing trained frontier AI models, drawing on antitrust hist
AI ResearchMachine LearningPolicyCopyrightAn analysis of the GPT-5.5 Codex reasoning-token clustering anomaly at 516/1034/1552 tokens, its statistical evidence, and implications for AI reasoning budgets
OpenAICodexAI ResearchLanguage ModelsNew research traces character-level jailbreaks to a structural cause: BPE tokenization fragments safety-critical tokens absent from alignment training data.
LLM SafetyTokenizationAlignmentJailbreaksKara addresses KV cache bloat in reasoning LLMs using sliding-window bidirectional attention and a Token2Chunk module to improve throughput without sacrificing
LLM InferenceKV CacheReasoning ModelsEfficiencyWiola introduces five novel components for small language models, including 3D helical positional encoding and dynamic token merging, with full HuggingFace inte
Small Language ModelsTransformer ArchitectureNLP ResearchEfficient AIA new paper shows GRPO, Dr. GRPO, and DAPO are three operations on the group reward standard deviation, unifying popular RLVR training methods.
Reinforcement LearningLanguage ModelsPolicy OptimizationRLVRA typed tuple protocol organises neural network component analyses into queryable, composable units, substantially outperforming unstructured retrieval baseline
Mechanistic InterpretabilityNeural NetworksRetrievalXAIA review of a 2026 ACL workshop paper proposing latent space steering vectors and calibrators to improve LLM control and output trustworthiness.
InterpretabilityLLM SafetyCalibrationNLPNew research isolates when natural-language feedback produces genuine improvement in LLM agents, beyond what repeated sampling alone can explain.
Language ModelsAI AgentsEvaluationReasoningLearnStop shows learned checkpoint stopping beats scalar exits on free-form math but not multiple-choice tasks. A cost-aware study across 18 task-model settings
Reasoning ModelsEfficiencyTest-Time ComputeEarly ExitA geometric proof that deterministic few-step text generation fails due to sharp categorical readouts, not training deficiency. Introduces DABI and CCI diagnost
Diffusion ModelsText GenerationGenerative ModelsNLPDynaSteer steers LLM reasoning trajectories toward truth using Fisher-LDA and entropy monitoring, outperforming prompting and static RepE baselines.
LLM ReasoningRepresentation EngineeringInference-Time InterventionHallucination MitigationA theoretical framework recasts language generation in the limit as a recall-precision trade-off, showing controlled hallucinations can improve coverage.
Language TheoryLLMsFormal MethodsNLP TheoryA new study shows that held-out selection gates, not artifact design, are what make recursive LLM agent self-evolution reliable across diverse benchmarks.
LLM AgentsSelf-ImprovementPrompt EngineeringBenchmarkingA new axiomatic framework exposes systematic representational failures in LLM latent reasoning that downstream benchmark accuracy consistently masks.
LLMsReasoningEvaluationInterpretabilityA new three-stage training method gives LLM agents genuine world-model planning ability, outperforming baselines on reasoning and search tasks.
LLM AgentsWorld ModelsReinforcement LearningPlanningA position paper argues the term machine unlearning is misused in LLM research, conflating dataset deletion with policy-driven suppression, with real consequenc
Machine UnlearningLLMsAI SafetyBenchmarksA critical analysis of a Bayesian framework for distilling knowledge from closed-source LLMs like GPT-4, bypassing the soft-label access problem using proxy mod
Knowledge DistillationLarge Language ModelsBayesian MethodsLLaMAAlgoEvolve uses LLMs as semantic mutation operators in a bi-level evolutionary framework for algorithmic trading, achieving an annualised Sharpe ratio of 5.60.
Algorithmic TradingLLMEvolutionary ComputationProgram SynthesisWhen AI benchmarks saturate, should we retire them? New research shows saturated benchmarks still yield rich insights across six performance dimensions.
BenchmarkingAI AgentsEvaluationReproducibilityA new LLM-powered pipeline compares DAO and corporate governance of AI agent protocols, finding comparable inequality but denser discourse alignment in open set
AI GovernanceDAOsLLM ResearchBlockchainA new iterative data pipeline isolates linearly scalable activation features for sycophancy, outperforming LLM-as-a-judge baselines with lower compute overhead.
AI AlignmentMechanistic InterpretabilityLanguage ModelsSycophancyNew research shows LLM refusal behaviour is controlled by persona representations at late layers, not just a standalone safety direction. Refusal rates drop fro
LLM SafetyMechanistic InterpretabilityActivation SteeringAI AlignmentNew research shows reinforcement learning on beneficial behaviour traits produces broad alignment generalisation across 50+ independent benchmarks, resisting ad
AI SafetyReinforcement LearningAI AlignmentLanguage ModelsNew research shows 125 Wikipedia edits by animal welfare advocates measurably influence how large language models discuss animal welfare topics.
LLMsTraining DataWikipediaData AttributionA new continuous metric reveals that small language models match large ones at factual extraction in RAG, while large models frequently override external eviden
RAGLLM EvaluationSmall Language ModelsNLPNew research shows that finetuning LLMs to recognise their own outputs can prevent and reverse emergent misalignment by stabilising model character identity.
AI SafetyLLM AlignmentFinetuningEmergent MisalignmentA controlled weight-space comparison of six offline RL training methods reveals that SFT, RFT, and RIFT converge on nearly identical weight updates, while DPO o
Mechanistic InterpretabilityOffline RLReasoningLoRAMeta employees petition against the MCI program collecting keystrokes, mouse data and screen content for AI training, raising serious privacy and consent questi
MetaAI EthicsPrivacyEmployee RightsAgenticRei proposes deontic policy enforcement for LLM-driven agents, adding obligations, dispensations, and conflict resolution beyond simple permit/deny engin
AI SecurityPolicy EnforcementAgentic AIDeontic LogicA forecast-grounded agentic system routes LLM decisions through structured evidence to reduce false interventions in DeFi risk supervision.
DeFiLLM AgentsFinancial AIRisk ManagementDeepSeek-V4 achieves 1M-token context with only 27% of prior inference FLOPs via hybrid compressed attention and MoE architecture innovations.
LLMEfficiencyLong ContextMoEA unified evaluation of eight diffusion language models across eight benchmarks reveals critical quality-efficiency trade-offs and the outsized role of inferenc
Diffusion ModelsLanguage ModelsNLPBenchmarkingSPSD compresses conversational prompts on-device before cloud LLM inference, saving ~100 tokens per call while preserving response quality within a non-inferior
LLM EfficiencyEdge AIPrompt EngineeringEnergyCodeBlock selects syntactically complete code fragments for SFT supervision, matching or beating full-token training with just 1.9% of supervised tokens across
Code GenerationLLM Fine-TuningData SelectionProgram AnalysisGMA replaces dot-product attention with probabilistic routing through learned Gaussian components, achieving linear memory scaling with interpretable latent res
TransformersEfficient AttentionSequence ModellingProbabilistic MethodsJetFlow breaks the speculative decoding scaling ceiling by combining one-pass drafting efficiency with causal tree conditioning, achieving up to 9.64x speedup o
Speculative DecodingLLM InferenceEfficiencyNLPDivInit improves parallel agentic search by selecting diverse first-turn queries via MMR, gaining 5-7 points on multi-hop QA at matched compute cost.
Agentic AIInformation RetrievalTest-Time ScalingMulti-hop QANew research shows LLM KV caches store field-conditioned conclusions at aggregator tokens, enabling editable and composable cache operations at up to 14.9x lowe
LLM InferenceKV CacheTransformersMechanistic InterpretabilityRepSelect uses SVD on forget-set gradients to isolate forget-specific representations, achieving 4-50x better resistance to relearning attacks than existing LLM
LLM UnlearningMachine LearningAI SafetyRepresentation LearningNew research shows repetition loops in Gemma 4 models trace to tiny sets of MLP neurons, editable with weight surgery while preserving benchmark scores.
Mechanistic InterpretabilityModel EditingLLMsGemmallada.cpp accelerates diffusion LLM inference on smartphones 17-42x over CPU baselines by aligning parallel denoising with mobile NPU execution characteristics.
Mobile AIDiffusion ModelsOn-Device InferenceNPUAnalysis of how Anthropic's own public policy positions may have legally and politically enabled the US export control directive restricting Claude access to fo
AI PolicyRegulationAnthropicExport ControlsMultiple state attorneys general are investigating OpenAI. An analysis of the legal, technical, and governance implications for AI regulation in 2026.
OpenAIAI RegulationAI GovernanceAntitrustArbor uses stateful tree search and multi-agent coordination to autonomously optimise full-stack LLM inference, achieving up to 193% throughput gains over vendo
Multi-Agent SystemsLLM InferencePerformance OptimisationAutonomous AIA compute-efficient family of Lean theorem provers where a 4B model outperforms a 671B baseline, using curriculum learning and structured data augmentation.
Theorem ProvingFormal VerificationLanguage ModelsMathematical ReasoningToolSense reveals a knowledge-retrieval dissociation in parametric tool retrieval: models scoring 90%+ on standard benchmarks collapse by 50-64pp on realistic q
LLM AgentsBenchmarkingTool RetrievalFine-tuningBlendIn addresses the intervention paradox in LLM alignment by blending model distributions rather than making binary accept/reject decisions, achieving up to 5
LLM AlignmentInference-Time MethodsLanguage ModelsAI SafetyA new evaluation framework tests whether sycophancy-reduction steering in LLMs also suppresses factually correct agreement, revealing structured non-specificity
AI SafetyInterpretabilityActivation SteeringSycophancyNew research shows KG triple format captures 2-3x more attention than equivalent natural language, compressing demonstration attention by up to 42% regardless o
RAGIn-Context LearningAttention MechanismsKnowledge GraphsEngram retrieves a 9.6k-token context slice that scores 83.6% on LongMemEval S, beating the 79k-token full-history baseline by 10.4 points.
LLM AgentsMemory SystemsKnowledge GraphsRetrievalA systematic mechanistic study of six alignment algorithms across three model families reveals that preference optimisation induces qualitatively distinct inter
Mechanistic InterpretabilityAlignmentLanguage ModelsPreference OptimisationA new fine-tuning method uses probabilistic programs to generate calibrated training targets, improving LLM inductive reasoning and uncertainty estimation.
LLMsProbabilistic ProgrammingReasoningCalibrationPolyFact dataset and GRPO-based reinforcement learning improve cross-lingual factual recall in LLMs without large-scale retraining, outperforming SFT across 12
Multilingual NLPReinforcement LearningLLMsFactual QAA new benchmark reveals that no current LLM achieves more than 33% distributional fidelity, exposing a fundamental gap in stochastic generation capability.
LLMsBenchmarksProbabilistic ReasoningEvaluationFAIR-Calib addresses a critical failure mode in diffusion LLM quantization by protecting fragile commit decisions at the write frontier during W4A4 PTQ.
QuantizationDiffusion ModelsLanguage ModelsPTQA new framework identifies two distinct failure modes in LLM reasoning traces using only token-level uncertainty signals from a single completion.
LLMReasoningUncertainty QuantificationNLPWAV v1 augments block residual routing with directional detail bases, improving validation loss at 48 layers on TinyStories and Text8 character-level tasks.
TransformersResidual ConnectionsDeep LearningLanguage ModelingA deep technical analysis of the perceptron model, covering weights, bias, decision boundaries, normalisation, and why this 1958 idea underpins modern neural ne
Machine LearningNeural NetworksPythonPerceptronA rare dataset from a discontinued Reddit experiment reveals how covert LLM agents systematically deployed identity, authority, and cognitive bias tactics to pe
AI EthicsLarge Language ModelsPersuasionSocial MediaGITCO improves frozen time series foundation model forecasts by detecting and suppressing disruptive input patches at inference time, achieving +1.95% MASE redu
Time SeriesFoundation ModelsInference-Time OptimizationForecastingPACT replaces free-form inter-agent messages with compact action-state records, cutting token usage by up to 50% while matching or improving task performance.
Multi-Agent SystemsLLM EfficiencyAgentic AINLPA new bilayer SIR framework models AI training data contamination as an epidemic, finding supercritical dynamics and identifying detection-based filtering as th
Model CollapseSynthetic DataEpidemic ModellingLLMsA new geometric framework shows LLM benchmark suites have effective dimensionality under 5, creating blind spots 100x larger than observed score gaps between to
LLM EvaluationBenchmarksGeometric TheoryMachine Learning TheoryA systematic study of QKV projection sharing in transformers finds K=V sharing cuts KV cache by 50% with only 3.1% perplexity degradation at 300M scale.
TransformersAttention MechanismsInference EfficiencyLanguage ModelsPOLARIS uses GRPO with LLM-judge rewards and human-reference injection to train a 9B model competitive with 27B models on long creative writing tasks.
NLPCreative WritingReinforcement LearningLanguage ModelsA large-scale study across 5 models, 10 datasets, and 4 retrieval methods finds biomedical RAG yields only marginal gains over no-retrieval baselines.
RAGBiomedical NLPLLM EvaluationMedical AIAURA-Mem replaces growing KV-caches with a fixed 4,224-byte gated fast-weight state, cutting memory writes 5-9x while matching accuracy on robot policy tasks.
RoboticsMemory SystemsEmbodied AITransformersNew research shows deeper transformer layers benefit from context-free value vectors, proposing Bank of Values to improve LLM performance with less compute.
TransformersAttention MechanismsLLM ArchitectureNLPCan graph mind maps improve LLM reasoning as visual scaffolds, not just knowledge sources? New research reveals a clear modality gap favouring visual guidance.
Large Language ModelsGraph ReasoningVisual AIKnowledge DistillationBitsMoE uses SVD decomposition and integer linear programming to allocate bits across spectral components, improving 2-bit MoE LLM accuracy by up to 27.83 point
LLM CompressionQuantizationMixture of ExpertsEfficient InferenceDOPA addresses in-context learning under distribution shift by using OOD proxies to retrieve source-domain demonstrations without any target-domain access.
In-Context LearningOut-of-DistributionLLMsNLPSENSE replaces lexical matching in retrieval-based speculative decoding with semantic embeddings, achieving up to 3.26x speedup across LLaMA and Qwen model fami
LLM InferenceSpeculative DecodingNLPEfficiencyPhyDrawGen uses a neuro-symbolic pipeline to generate physically accurate diagrams from text, outperforming GPT-5 and Gemini on force accuracy by 5x.
AI ResearchComputer VisionNeuro-Symbolic AIPhysicsQASM-Eval is the first dataset targeting LLM training and evaluation on OpenQASM 3's hardware-facing features, with fine-tuned Llama-70B reaching 85% pass@1.
Quantum ComputingLLMsCode GenerationBenchmarksA new protocol uses RAG-enabled majority voting across ChatGPT models to generate and verify disease-centric biomedical associations, exposing hallucination sys
LLMsBiomedical NLPRAGHallucinationA critical analysis of how RLVR post-training shapes LLM linguistic patterns, and what AI detection systems reveal about automated assessment and human expressi
LLMPost-TrainingAI DetectionRLHFA critical analysis of Rotary GPU, an exploratory approach to executing large Mixture-of-Experts models locally on consumer hardware with severely constrained V
LLM InferenceMixture of ExpertsEdge AIGPU MemoryNew research tests LLM peer review alignment on 984 ACL submissions, finding limited reliability and that iterative LLM edits can game scores in specific scenar
Peer ReviewLarge Language ModelsNLPAcademic IntegrityThe Cognitive Categorical Transformer reaches 21.27 PPL on WikiText-103, beating a matched GPT-2 Small baseline by 12% through category-theoretic structural pri
Language ModelsCategory TheoryArchitectureNLPNew mechanistic interpretability research shows RL fine-tuning preserves internal attention circuits 13.5% better than SFT, explaining its resistance to catastr
Mechanistic InterpretabilityFine-TuningReinforcement LearningCatastrophic ForgettingICG combines multimodal LLMs with diffusion models and multi-reward learning to generate personalised cover images without ground-truth labels.
Multimodal AIImage GenerationRecommender SystemsDiffusion ModelsA three-module LLM pipeline detects and quantifies human values in text across any theoretical framework, showing architecture matters more than model choice.
AI EthicsNLPValue AlignmentLarge Language ModelsNew research challenges claims that LLMs can monitor their own internal states, showing that apparent introspection reduces to input-level pattern matching.
LLMsMetacognitionInterpretabilityAI ResearchGEM reformulates LLM data curation as a variational problem on the hypersphere, improving downstream accuracy by up to 1.2% over strong baselines.
LLMData CurationPretrainingClusteringSelf-Verified Distillation lets LLMs improve reasoning using only unlabeled prompts, achieving +16.7 points on math benchmarks with no external teachers.
LLMsSelf-TrainingReasoningSynthetic DataA preregistered study of 11 LLMs finds systematic overconfidence that worsens with task difficulty, mirroring well-known human calibration biases.
LLMCalibrationReasoning ModelsAI SafetyNew research shows 61-93% of reasoning steps in frontier LLMs are redundant, and proves this overthinkin is a structural training artefact.
LLM ReasoningChain of ThoughtModel EfficiencyReinforcement LearningA new framework derives optimal token allocation policies for multi-agent LLM workflows, balancing latency, reliability, and cost using convex optimisation.
LLMMulti-Agent SystemsOptimisationAI SystemsFuRA achieves full-rank weight updates with LoRA-level efficiency by freezing SVD bases, outperforming full fine-tuning on LLMs and VLMs.
Fine-TuningPEFTLLMsOptimizationLatent Cache Flow replaces text-based LLM agent communication with compressed KV cache transfer, cutting adapter size 73x and latency 8.5x over baselines.
LLM AgentsMulti-Model SystemsKV CacheInference EfficiencySmall language models copy the last number before an answer delimiter rather than reasoning through CoT steps. A new study exposes the mechanism across three ar
Chain-of-ThoughtMechanistic InterpretabilityArithmetic ReasoningLanguage ModelsA technical deep-dive into Bayesian Gaussian process modelling where spatial coordinates are themselves uncertain, with applications to geostatistical inference
Bayesian InferenceGaussian ProcessesSpatial StatisticsGeostatisticsICE awarded Bi2 Technologies a $25.1M no-bid iris-scanning contract. A technical and policy analysis of the biometric system, its risks, and missing safeguards.
BiometricsSurveillanceAI PolicyCybersecurityA new impossibility theorem proves no feature ranking can be faithful, stable, and complete when features are collinear, with formal Lean 4 verification.
Explainable AIFeature AttributionSHAPFormal VerificationA new model-agnostic attribution score uses Bayes rule and stochastic process theory to explain LLM token importance without requiring model internals.
ExplainabilityLLMsNLPXAISOLAR proposes a self-optimizing agent that treats LLM weights as an explorable environment, outperforming strong baselines across reasoning tasks without gradi
Continual LearningMeta-LearningLarge Language ModelsReinforcement LearningA position paper proposes synthetic data probes generated from known distributions to systematically study how data properties drive LLM behaviour and performan
Large Language ModelsData ScienceResearch MethodologyInformation TheoryNew research finds LLMs produce overly idealised portrayals of disability, erasing real struggles. A comparison with Reddit posts reveals systematic bias.
LLM BiasDisabilityAI FairnessNLPA neural framework estimates pairwise mutual information from masked diffusion model hidden states, enabling 3-5x faster parallel decoding while preserving gene
Diffusion ModelsGenerative ModelsProtein DesignInference EfficiencyA new two-dimensional framework combines cognitive function and execution topology to classify 27 AI agent design patterns, resolving ambiguities in existing ap
AI AgentsLLMSystem DesignMulti-Agent SystemsAgentWall intercepts AI agent actions before execution, enforcing declarative policies with 92.9% accuracy and sub-millisecond overhead across major coding envi
AI SafetyAutonomous AgentsSecurityDeveloper ToolsHow interpretability tools can resolve AI annotator disagreement in safety policies. A deep dive into arXiv research.
AI SafetyInterpretabilityMachine LearningData AnnotationA mechanistic study of three VLM families finds attention sharpness is a near-zero predictor of correctness, while hidden-state geometry reaches AUROC>0.95.
Vision-Language ModelsMechanistic InterpretabilityAI ReliabilityAttention MechanismsUCCI uses isotonic regression to calibrate LLM confidence scores, cutting inference costs 31% on a 75k-query NER workload while maintaining micro-F1 of 0.91.
LLMInferenceUncertainty CalibrationModel RoutingNew research finds DP training reduces bias in sentence scoring tasks but fails to generalise, revealing a gap between logit-level and output-level fairness.
Differential PrivacyFairnessLLMsNLPGraphBit replaces prompted LLM orchestration with a Rust-based DAG engine, achieving 67.6% on GAIA benchmarks with zero framework-induced hallucinations.
Agentic AILLM FrameworksMulti-Agent SystemsBenchmarksHELLoRA targets LoRA adapters at the most-activated experts in MoE models, cutting trainable parameters by 84% while improving downstream task accuracy.
Fine-TuningMixture of ExpertsParameter EfficiencyLLMsNew research shows invisible orchestrators in multi-agent LLM systems suppress protective behaviour and distort internal states, undetectable by output-based ev
AI SafetyMulti-Agent SystemsLLM ResearchEnterprise AILBW-Guard adds a bounded control layer above AdamW to prevent training instability in LLMs, cutting perplexity 18.7% and surviving extreme learning-rate stress.
Machine LearningLLM TrainingOptimizationTraining StabilityA new attack targets the acceptance mechanism of speculative decoding, collapsing inference speedup while preserving output quality and evading detection.
LLM SecuritySpeculative DecodingAdversarial MLInference EfficiencyOP-Mix uses low-rank adapter interpolation to optimise data mixtures across pretraining and continual learning, cutting compute by up to 95% vs baselines.
Data MixingContinual LearningPretrainingLoRAPREPING tackles the cold-start problem in AI agents by constructing procedural memory from synthetic practice before any real tasks are observed.
Agent MemoryLLM AgentsAI PlanningSynthetic DataNew research shows 3-bit quantization causes 6-21% of previously unbiased LLM outputs to develop stereotypical bias, invisible to standard quality metrics.
LLMsQuantizationAI SafetyBiasA systematic 10-phase study reveals that CUDA optimisation insights fail on Apple M3 Ultra, achieving 22.7 FPS img2img via CoreML and model distillation.
Diffusion ModelsApple SiliconInference OptimizationCoreMLREVELIO systematically finds interpretable failure modes in Vision-Language Models using beam search and Gaussian-process sampling, revealing safety risks in au
VLM SafetyInterpretabilityAutonomous DrivingAI ResearchThe performance and architectural implications of running local LLMs on Apple M4 with 24GB unified memory. A technical deep-dive.
Local AIApple SiliconLLM InferenceEdge ComputingNew research identifies logarithmic decay laws governing routing accuracy in LLM agent skill libraries, with actionable optimisations lifting pass rates signifi
LLM AgentsScaling LawsNLPAI ResearchTeamTR addresses a structural failure in multi-agent LLM fine-tuning, proving quadratic penalty scaling and achieving 7.1% gains over single-agent baselines.
Multi-Agent SystemsLLM Fine-TuningReinforcement LearningICML 2026TraFL addresses trajectory locking in diffusion LM post-training, improving mathematical reasoning and code generation across all benchmark settings.
Diffusion ModelsLanguage ModelsReinforcement LearningPost-TrainingA new Conv-VaDE model jointly learns topographic reconstruction and soft clustering for EEG microstates, achieving 0.730 GEV with principled architecture search
EEGNeuroscienceDeep LearningClusteringNew research shows LLM diversity collapse stems from probability miscalibration during decoding, not just sampling heuristics. A framework across 14 models.
LLMsSamplingCalibrationNLPHow AI is tackling healthcare's hidden back-office crisis, and what it means for the future of medical admin and patient care.
AI in HealthcareMachine LearningHealthcare AutomationFBI seizure of NetNut and the Popa botnet exposes deep flaws in residential proxy ecosystems. Technical analysis of the takedown and its wider implications.
CybersecurityBotnetsFBIResidential ProxyAn analysis of a bizarre email validation scheme that routes addresses through a spam network, examining the technical and ethical failures involved.
EmailSpamCybersecurityWeb DevelopmentTwo Scattered Spider members pleaded guilty over the TfL cyberattack. An in-depth analysis of their TTPs, the SIM-swapping infrastructure, and what this means f
CybersecurityRansomwareSocial EngineeringSIM SwappingLLMs have disrupted the economics of security research. This analysis examines what that means for coordinated disclosure, open source maintainers, and triage w
CybersecurityOpen SourceLLMsVulnerability DisclosureAnalysis of the Popa botnet's architecture, its links to NetNut and Alarum Technologies, and what it reveals about the residential proxy economy's systemic risk
CybersecurityBotnetsResidential ProxiesAndroid SecurityMicrosoft's record-breaking June 2026 Patch Tuesday reveals how AI-assisted vulnerability discovery is reshaping the threat surface. A technical analysis of the
CybersecurityVulnerability ResearchAI SecurityMicrosoftDutch authorities arrested two men and seized 800 servers linked to Stark Industries Solutions, exposing how sanctioned Russian cyber infrastructure evades EU e
CybersecurityRussian Cyber OperationsEU SanctionsDDoS InfrastructureKrebs on Security traces The Gentlemen ransomware admin through OSINT pivots, linking forum handles to Alexander Yapaev of Izhevsk, Russia. A technical analysis
CybersecurityRansomwareOSINTThreat IntelligencePro-Iranian hackers exploited Meta's AI support bot to hijack Instagram accounts, exposing critical flaws in AI-driven account recovery workflows.
AI SecurityMetaInstagramSocial EngineeringPromptArmor's disclosure reveals critical indirect prompt injection vulnerabilities in ChatGPT for Google Sheets, enabling cross-workbook data exfiltration and
AI SecurityPrompt InjectionLLM VulnerabilitiesGoogle SheetsA CISA contractor exposed AWS GovCloud credentials, plaintext passwords, and internal secrets on a public GitHub repo. Analysis of what went wrong and why it ma
CybersecurityAWSGitHubGovernment SecurityTechnical analysis of the Kimwolf IoT botnet arrest, examining DDoS infrastructure, device exploitation patterns, and the operational security failures that led
CybersecurityIoT SecurityDDoSBotnetsA CISA contractor published AWS GovCloud keys and agency secrets to public GitHub. Analysis of the technical failures, institutional collapse, and CI/CD pipelin
CybersecurityCISAGitHubCredential ExposureThe Brazilian anti-DDoS firm botnet scandal, exploring insider threat models, DDoS-for-hire economics, and cybersecurity ethics.
DDoSBotnetCybersecurityNetwork SecurityThe Canvas data extortion attack targeting 275M students, exploring EdTech security failures and systemic vulnerabilities in education p
CybersecurityEdTechData BreachEducation TechnologyFreeBSD-SA-26:13, a local privilege escalation via execve(). Deep technical analysis of kernel exec path vulnerabilities.
FreeBSDKernel SecurityPrivilege EscalationCVE AnalysisMicrosoft's record 167-CVE Patch Tuesday, the SharePoint zero-day, BlueHammer, and what this means for enterprise security in 2026.
Patch TuesdayCybersecurityMicrosoftZero-DayAI-assisted vulnerability discovery is driving record patch volumes across Apple, Google, Microsoft, and Oracle. An analysis of what this means for software sec
AI SecurityVulnerability ResearchPatch TuesdayAnthropicRussia's GRU router exploit campaign harvesting Microsoft Office tokens across 18,000+ networks, implications for zero-trust security.
CybersecurityState-Sponsored HackingMicrosoft OfficeRouter SecurityThe Scattered Spider guilty plea, examining SMS phishing TTPs, social engineering at scale, and what this means for enterprise security
Scattered SpiderCybercrimePhishingSocial EngineeringClojureScript's new async/await support, analysing what this means for functional programming, AI tooling, and modern web development.
ClojureScriptFunctional ProgrammingAsync AwaitJavaScriptExploring what the Art Institute of Chicago's boolean obscurity field reveals about digital curation, long-tail data, and the architecture of cultural APIs.
Open DataCultural ComputingAPI DesignDigital HumanitiesTexas A&M researchers report nasal delivery of a compound reverses brain aging markers in mice. A critical analysis of the methodology, mechanisms, and open que
NeuroscienceAgingBiotechnologyDrug DeliveryCreativityBench, a new benchmark testing whether AI agents can repurpose tools creatively through affordance-based reasoning.
Artificial IntelligenceMachine LearningLLM BenchmarksCreative AIA technical analysis of Grid2Poster, an open-source tool that generates print-ready transmission grid posters from OpenStreetMap data using GeoPandas and Matplo
Open SourceEnergy InfrastructureGeospatialOpenStreetMapOpenAI's new voice intelligence API features and what they mean for developers, security, and the future of AI.
OpenAIVoice AIAPIMachine LearningNew research showing unconscious language processing and what it means for AI, NLP, and machine learning architectures.
NeuroscienceArtificial IntelligenceNLPMachine Learning