Netflix Wiz creates app to slash AI bills, then open sources it
Netflix Wiz created an app to reduce AI bills and then open sourced it. The tool helps optimize AI infrastructure spending.
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Netflix Wiz created an app to reduce AI bills and then open sourced it. The tool helps optimize AI infrastructure spending.
Babysitter is an open-source framework enforcing obedience on agentic workforces to manage complex tasks and workflows through deterministic, hallucination-free self-orchestration.
OpenAI restarts robotics team five years after shutdown, emerging from world simulation research program. Near-term goal: infrastructure robots. Long-term vision from CEO Sam Altman: personal robot for everyone.
Comparative benchmark of MTP (Multi-Token Prediction) quantizations between unsloth and bartowski on Qwen 3.5-4B, 3.5-9B, and 3.6-27B. Bartowski uses Q8_0 for MTP head (larger files). Tests for Snapdragon with Q4_0, IQ4_NL, Q4_1, MXFP4_MOE, Q8_0 limited to 24GB VRAM RTX 3090. Unsloth generally faster in decoding throughput and VRAM efficient.
A remote code execution (RCE) vulnerability discovered in Odysseus Chat, a PewDiePie-related project. A fix is being submitted via pull request.
User deploys Deepseek V4 Flash on DGX Spark (2x ASUS GX10) via vLLM. Max context 256k tokens, prefill throughput 1680-2150 T/s, decode 37-49 T/s across window sizes. Consistent performance, low degradation. Model outperforms M2.7 and Stepfun 3.7 on high-context reasoning benchmarks.
Antitech offers free security assessments for AI agents and LLM-powered workflows. The company tests agents against prompt injection, tool abuse, data leakage, and guardrail bypasses. Participants receive vulnerability reports and future discounts.
French State invested €404 million in March to acquire Bull from Atos, asserting sovereignty in high-performance computing. Foxconn establishes a factory in Angers, raising concerns about French technological independence amid global supply chains.
SoftBank commits €45 billion in France for AI data center development in an initial phase, with €75 billion potential subject to expansion. The project targets up to 5 GW capacity.
Dell delivered the first Vera Rubin NVL72 server to CoreWeave, integrating 72 GPUs in a single rack. This infrastructure marks a key milestone in compute density for AI.
BYD launches its Xuanji A3 chip for autonomous driving, reducing reliance on Nvidia. The Chinese automaker aims to control its value chain by developing proprietary autonomous driving solutions.
Qwen 3.7 Plus from Alibaba is now available on Vercel AI Gateway. This multimodal model unifies vision and language for AI agents, covering GUI/CLI, coding, and visual tasks. Access via SDK with transparent pricing and zero platform fees.
VibeETL: open-source visual ETL platform built in 3 months by former data scientist. Polars + Rust backend, React Flow frontend with native BFS layout algorithm. Zero external dependencies, sandboxed Python execution (30s timeout). Lightweight Alteryx alternative.
NVIDIA releases Cosmos 3, an open omni-model for physical AI that reasons and acts. The model processes video, text, and images to understand real-world physics and generate robotic actions.
A Reddit user reports a model (likely Minimax M3) trained on 100+ trillion tokens, double current standards (27-50T for Kimi, Mimo, Deepseek). Author doubts the model exceeds 500B parameters despite this massive data scaling.
Anthropic adds a new control to manage message limits on Claude. The feature improves visibility without fully solving the quota problem.
XLGoBench is a benchmark of synthetic algorithmic tasks to detect cross-lingual gaps in LLM abilities. The benchmark is commensurate across languages, scalable (variable complexity), quantifiable (objective correctness), and transparent (auditable templates). Experiments reveal persistent cross-lingual gaps in multiple state-of-the-art models.
SAGE is an adaptive gate using von Mises-Fisher density estimation to control memory evolution in agentic LLMs. It classifies candidate facts as ADD (novel), NOOP (redundant), or MERGE (uncertain), reducing expensive LLM calls. On LoCoMo, SAGE cuts API cost by 3.4× and latency by 2.5× with GPT-4o-mini.
TraceGraph is a graph-based framework that transforms multi-model agent trajectories into shared decision landscapes. It builds graphs over state-action-observation spaces, identifies productive cores and trap regions, then proposes a trap-aware recovery pipeline. On SWE-bench, this approach improves resolution rate from 40.4% to 43.5%.
CoSee, an auditing framework, analyzes failure modes of modular visual reasoning systems using shared working memory. On 4B–8B models, two dominant failure modes emerge: Noise Reinforcement (reusing ungrounded notes) and Policy Collapse (under-specified answers). The study shows naive shared workspaces amplify hallucinations without explicit verification.
TeachObs is a human-validated multimodal benchmark for classroom video analysis. It contains 30 public lessons from 8 countries split into 5,158 15-second scenes, annotated by 7 researchers with 39 observation codes (20 visual, 19 non-visual). Evaluation of 5 vision-capable LLMs across 3 tasks: no single model consistently outperforms others.
GLIDE is an open-source Python library unifying prediction-powered inference methods (PPI++, Stratified PPI, Predict-Then-Debias) for evaluating agentic systems. It combines human annotations and LLM judgments into unbiased estimates with valid confidence intervals, reducing annotation costs while maintaining precision.
Scientific ML framework for turbine Remaining Useful Life (RUL) prediction. Shared encoder (CNN + bidirectional LSTM + attention pooling) with task-specific heads predicts turbine gas temperature, Delta TGT, and RUL with quantified uncertainty intervals. Evaluated on heterogeneous real-world fleet data using MAE, PICP, MPIW, and coverage-width criterion metrics.
Benchmarking of 5 uncertainty quantification methods (Delta, Bayesian Monte Carlo Dropout, Bootstrap, LUBE, MVE) for turbine gas temperature degradation prediction. Evaluation on real dataset using coverage probability and prediction interval width metrics. Trade-offs identified between accuracy and reliability.
Study on encoding factored tasks (FTS) into SAT for planning. Authors propose multiple strategies for translating the factored transition relation into propositional logic and analyze the impact of task transformations and parallelism on SAT-based planners.
Study applying MAP-Elites (quality diversity algorithm) to procedural generation of FPS levels. Two novel representations (Point-Line, Spatial-Layout) improve map characterization. Topological and emergent metrics defined. MESB generates map populations with higher diversity and quality than previous approaches.
Study on harness self-evolution (prompts, skills, memories, tools) in LLM agents. Analyzes two capabilities: harness-updating (producing useful updates) and harness-benefit (benefiting from them). Findings: harness-updating is capability-agnostic (Qwen3.5-9B matches Claude Opus gains), while harness-benefit is non-monotonic (mid-tier models benefit most).
EHRBench is an automated and reliable benchmark for evaluating LLMs on clinical decision-making tasks. Built via an EHR-LLM-KB pipeline, it generates ~960k QA items covering diagnosis, treatment, and prognosis. 30+ LLMs benchmarked reveal persistent gaps toward clinical reliability.
Process-Level Latent Variable Model (PLVM) predicts future behavioral strategies from partial cross-task process traces. Tested on PowerWash Simulator: fusing traces from two cleaning tasks predicts whether a player adopts "Zone Planner" or "Zone Hopper" strategy on unseen Fire Station level. Applicable to adaptive systems (tutors, games, human-AI collaboration).
arXiv paper on autonomous agentic data engineering for model specialization. GPT-5.2 constructs a training curriculum improving a student model by 57.29% through iterative, agent-driven data adaptation. Formalizes a novel task evaluating LLMs as autonomous data engineers.
Activation steering study across four multilingual LLMs (5 figurative categories, 6 languages). Directions learned in one language transfer effectively to others, particularly German. Composite cross-lingual directions match or exceed native directions, providing direct evidence of reusable but target-dependent figurative signals across languages.
CSRM (Configurable Safety Reward Model) jointly optimizes calibrated safety compliance and reward modeling to adapt LLMs to heterogeneous and evolving safety requirements. Achieves 94.6% F1 on CoSApien and 75.8% F1 on DynaBench without additional human annotation.
DisasterLex is a knowledge-graph-mediated text-to-SQL framework for querying geospatial disaster-analytics databases. It uses an Expert Knowledge Graph (107 concepts, 117 causal edges) to route natural-language queries across 36 heterogeneous tables. On 75 test queries, it outperforms 4 baselines (LightRAG, HippoRAG 2, ReFoRCE, CHESS) by 1.4x to 2.75x.
5WBENCH, a balanced 5,000-sample benchmark across 5W categories, reveals unlearning methods fail on causal (Why) questions. MAAT, a three-phase framework operating on LoRA weights, combines gradient-projected ascent, SVD rank pruning, and KL-hidden-state repair to simultaneously achieve high forgetting and retention on causal knowledge.
MAVEN is a lightweight symbolic reasoning scaffold to improve generalization of LLM agents in tool-calling tasks. Evaluated on BFCL v3, TauBench, Tau2Bench, AceBench and a new MAVEN-Bench benchmark, it increases GPT-OSS-120b accuracy from 48% to 71% without additional training, at roughly 1/10 the cost of proprietary baselines.
GRiD, a diffusion-model framework, generates graph-like rules for knowledge graph reasoning. Combines supervised pre-training and reinforcement learning to discover complex rules (cycles, branches) beyond simple chains. Evaluated on 6 benchmarks with open-source code.
Comparative study of zero-shot multi-label topic classification using knowledge graphs extracted from documents. Framework tested on 15 LLMs and 8 datasets: keyword-enhanced variant outperforms baseline, graph augmentation helps small models but hurts large ones, and self-consistency decoding increases costs fivefold without performance gains.
Study of global narrative dominance in LLMs via CulturalNB, a dataset of 717 Bengali cultural instances with parallel English-Bangla question-answer pairs. Evaluation of 9 LLMs shows English questions increase global substitution and reduce local perspective coverage, even with local evidence provided.
ImmigrationQA: source-grounded QA dataset of 17,058 pairs across 13 U.S. immigration law subdomains. Fine-tuned Llama 3.2 3B with LoRA on corpus of 10,056 validated documents. Fine-tuned model: 1.08/3.0 (16.8% fully correct) vs Llama 3 8B base: 0.85/3.0 (4% fully correct), 27% relative improvement. Cost: ~$29. Dataset, model, and code publicly released.
Researchers train a small encoder-decoder transformer on the zeta map, a classical bijection in q,t-Catalan combinatorics. Mechanistic interpretability tools (cross-attention analysis, linear probing, causal intervention) reveal a level-based mechanism. Translation into an explicit peak-centered traversal algorithm (scaffolding map) proven equivalent to the zeta map.
Researchers reveal that statistical watermarks in LLMs are vulnerable to linear ensembles. Averaging probability distributions across 3-5 models cancels out watermark perturbations. WASH (Watermark Attenuation via Statistical Hybridisation) defeats detection across 6 watermarking schemes, reducing z-scores from 5-300 to <2 (threshold: 4), while improving output quality by 27.5%.
Item Response Theory-based method detects mislabels in 7 LLM benchmarks at 95% precision on top 200 examples across 114 models. Analysis reveals errors from mechanical labeling heuristics, inherited annotation mistakes, and fundamentally ambiguous items. Reward models specialize in stylistic preference over factual knowledge; one frontier model agrees with detected mislabels at 78% accuracy versus 38% for peers.
Comparative study of generic vs domain-specific embeddings for multilingual clinical search (ICD-10-CM). A bi-encoder fine-tuned on Gemini-generated synthetic data (6 languages) outperforms BioBERT-ST: R@5=0.822 vs 0.790, with major gains in Portuguese (+0.115). Open recipe for LLM-based medical retrievers.
LLM-FACETS is an open-source framework for evaluating LLM factuality, epistemic calibration, and reproducibility. Web interface, plugin architecture, deterministic metrics (BLEU, ROUGE, BERTScore) run locally, log-probability visualization, multi-judge consensus, RAG Triad metrics. Designed for technical experts, domain experts, and compliance officers per EU AI Act and NIST standards.
Distributed approach for constrained multi-agent reinforcement learning combining state-augmented policy learning with consensus over Lagrange multipliers. Agents learn offline policies and coordinate via local communication. Linear scalability to thousands of agents, demonstrated on smart grid demand response.
Method to automatically generate fine-grained evaluation rubrics without human annotation, tested on four benchmarks. Training-free approach, then iterative fine-tuning via meta-judge reward signals. A fine-tuned 14B rubric generator outperforms larger proprietary models.
Researchers improve multilingual speculative decoding by comparing three strategies: fine-tuning draft models on task-specific data, fine-tuning on unlabeled monolingual corpora, and training n-gram draft models. Evaluation across 11 languages on translation and story generation tasks. N-gram models provide consistent speedups despite lower acceptance rates.
Method to detect and classify dataset usage in research literature using a multitask GLiNER framework. Combines dataset mention extraction, relation identification, and usage-context classification. Leverages synthetic data generation and LLM-based revalidation to address label scarcity.
COFT is a training-free decoding method that reduces biases in LLM chain-of-thought generation. It uses masked counterfactual prompts and logit fusion to attenuate attribute-driven biases, with distribution-free marginal validity guarantees. Evaluation across 6 models: 30-55% bias reduction (median 38%) with negligible utility loss and ≤11% computational overhead.
Multi-agent LLM systems assume agreement between agents indicates reliability. Authors show communication induces correlated failures and false consensus. They propose CAGE-CAL, a counterfactual agent-graph calibration framework comparing post-communication dependencies with no-communication scenarios to adjust confidence accordingly.
SLAT is an RL framework that reduces redundancy in chain-of-thought reasoning by selectively suppressing low-utility segments. On standard benchmarks, the method achieves 50% reasoning length reduction while maintaining competitive accuracy.
COMPASS is a safety alignment framework for multi-step LLM search agents. It combines Cognitive Tree Exploration (CTE) to synthesize stealthy attack trajectories and Introspective Step-wise Alignment (ISA) to supervise risky intermediate actions. Results: favorable safety-utility trade-off requiring substantially less training data.
New post-training method for reasoning models: Feedback Distillation trains the model to match its own distribution conditioned on LLM-generated feedback at token level. Tested on Lean4 theorem-proving, it maintains greater trajectory diversity than GRPO, improves policy entropy and pass@k scaling. Combined with GRPO, it outperforms either method alone.
UniScale unifies model routing and test-time scaling (TTS) in a single optimization space to balance LLM inference quality and computational cost. The framework uses LinUCB and contextual multi-armed bandit theory to learn adaptive inference policies online, with cost modeling and efficiency-aware learning.
Persona-based evaluation framework for pluralistic alignment in generative AI. Replaces monolithic benchmarks with structured manifold of synthetic cognitive profiles representing diverse human perspectives. Reveals systematic degradation of persona coherence under sequential inference, suggesting need for dynamic regulatory mechanisms.
Novel transformer-based architecture for autonomous resource management in heterogeneous satellite clusters (optical and SAR). Uses model-free reinforcement learning for real-time decision-making in Earth Observation missions. Demonstrates significant performance improvements and transferability across varying cluster sizes.
GraphARC is an AI benchmark for abstract reasoning on graph-structured data, generalizing the ARC paradigm to graph transformations. Current language models fail on full graph transformation tasks despite understanding graph properties, revealing a comprehension-execution gap.
Method to establish correspondences between embedding vectors from different black-box encoders. Exploits local geometric consistency of independently trained contrastive encoders: short-range distances preserved up to scale factor. Uses iterative reference-based geometric embedding hashing with paired anchors to recover vector links. Code released.
CobSeg is a multi-branch architecture for dialogue topic segmentation. It separates semantic continuity from lexical transitions and uses boundary informativeness weighting. Evaluated on 5 benchmarks, it reduces Pk by 0.7 points on VHF and achieves Pk=1.0 on DialSeg711, without LLM calls at inference.
Study of alignment between LLM uncertainty and human uncertainty through behavioral analysis and internal activation patterns. Authors measure calibration and alignment across multiple-choice and open-ended factual recall datasets, assessing impact of instruction fine-tuning.
ElasticMem introduces a learnable latent memory framework for LLM agents with adaptive retrieval and elastic budget allocation via learned policy. On Qwen2.5-3B and 7B backbones, achieves 26.2% and 24.6% QA accuracy gains, 66.3% and 27.2% ALFWorld success improvements, with lowest token cost.
Unified framework for gradient aggregation in multi-objective optimization. Authors establish convergence rates to Pareto stationarity via sufficient alignment condition, showing non-conflicting directions within gradient convex hull ensure convergence. Introduces capped MGDA from CVaR formulation, validated on synthetic and practical benchmarks.
Study of gender-specific neurons in language models (feminine, masculine, gender-neutral). Authors propose neuron-level intervention method to identify and control gendered language generation. Experiments on two open-source LMs show gender neurons concentrate in early layers. Code and datasets released.
MASA (Model-Aware Skill Alignment) adapts procedural skills for LLM agents to each model backbone without weight modification. A hierarchical evolution pipeline rewrites skills via hill climbing and UCB-driven tree search, then a lightweight rewriter trained on trajectories reproduces adaptation in a single forward pass. Gains up to 25.8 points across three interactive environments and four backbones.
Study of black-box LLM distillation through bounded behavioral indistinguishability. Authors evaluate Qwen and Llama pairs with 5,000-prompt suite, showing LoRA improves semantic similarity (0.788→0.862 for Qwen, 0.814→0.874 for Llama) but leaves detectable behavioral differences exploitable by adversaries.
New method to accelerate diffusion-based language models (dLLMs). Temporal-Spatial Parallel Decoding (TSPD) and Confidence Extrapolation (CE) reduce unnecessary denoising iterations by analyzing token-wise trajectories and forecasting future logit trends without additional training.
Theoretical paper defining pairwise reference alignment as an ordinal observable for language model evaluation. Formulates statistical framework to measure whether a model ranks preferred responses above rejected responses, with finite-sample estimators and concentration bounds. Empirical validation on Qwen2.5 and RewardBench.
Eywa is a provenance-grounded memory architecture for persistent AI agents, storing immutable source evidence before deriving facts and validating memories against typed signals. Retrieval uses a deterministic multi-route read path with zero LLM calls. Results: 90.19% judge accuracy on LoCoMo C1-C4, 88.2% on LongMemEval-S, 81.45% mean nugget score on BEAM.
Novel MADQI evaluation metric for unsupervised anomaly detection in maritime AIS datasets. Combines four indices (ARC, PPS, SDS, ECE) through automatic normalization. Achieves MADQI score of 80.37% on AIS data, with ECE=0.907 and ARC=1.000 for detecting abnormal vessel behavior.
Formalizes causal pathways for rare events in structural equation models. Proposes a formal definition of causal pathways and identifies conditions where testable implications depend only on the causal abstraction defined by the rare event pathway, rather than the full causal graph.
COLLEAGUE.SKILL is an automated trace-to-skill distillation system for generating person-grounded AI skills via expert knowledge extraction. The system produces versioned packages with two coordinated tracks: capability (practices, mental models, decision heuristics) and bounded behavior (communication style, interaction rules). 18.5k GitHub stars, 215 skills from 165 contributors.
PhyDrawGen is a neuro-symbolic pipeline generating physics diagrams from text while respecting physical laws. An LLM extracts a typed scene graph, a deterministic solver converts it to a planar straight-line graph, and Qwen-VL fine-tunes a propose-verify loop. Evaluated on 1,449 problems (mechanics, optics, electromagnetism), it outperforms GPT-5-image and Gemini.
Reinforcement learning framework for autonomous driving using uncertainty-aware expert advice with adaptive thresholds. Epistemic and aleatoric uncertainty trigger expert intervention; commitment-cooldown strategy prevents long-term dependence. CARLA experiments: +5-7% success rate vs IQN baseline.
√LTS algorithm for tree search with implicit rerooting. Three rerooter designs proposed: clustering-based on state-space structure, heuristic-based with cost-to-go estimates, and hybrid. Avoids explicit subgoal generation, reduces computational overhead, and achieves state-of-the-art online training efficiency on tested domains.
Researchers reframe healthcare mechanism design as program synthesis for LLMs. Medi-Sim, a multi-agent simulator, evaluates rule programs against strategic provider responses (coding, selection, delay, effort, triage). LLM-guided evolutionary code search synthesizes a mixed-objective program that eliminates up-coding, halves rejections, and retains baseline profitability.
AdaCoM, an external LLM system, manages context for frozen LLM agents via reinforcement learning on long-horizon tasks (web search, deep research). Learned strategies reveal a Fidelity-Reliability Trade-off: high-performing agents benefit from higher-fidelity context preservation, while lower-performing agents require aggressive compression.
DecomposeR, a deep research framework, trains Qwen3-8B in two RL stages: planner RL learns typed DAG structures and query decomposition, then answerer RL learns branch execution and synthesis. Achieves 5.1-8.0 point improvements on long-form benchmarks through explicit planning and structured rewards.
FAM-Bench is a 2500-instance multimodal benchmark verified by nutrition experts, evaluating Food-as-Medicine reasoning across 13 health conditions. Two tasks: assess dish suitability for a condition (image + ingredients) and rank 4 dishes by clinical relevance. Tests integration of nutritional constraints, visual cues, and ingredient evidence.
AutoSci is a memory-centric multi-agent system automating the full scientific research lifecycle. It combines SciMem (structured memory), SciFlow (5-stage execution), SciDAG (multi-agent operators), and SciEvolve (continuous learning). Code available on GitHub.
EUDAIMONIA is a benchmark evaluating harmful social dynamics in LLMs. It contains 969 user inputs and 3,147 design-violation checks, testing 22 recent models. Claude-Opus-4.7 and GPT-5.5 violate 30.7% and 27.2% of checks respectively, revealing persistent social-alignment failures not resolved by extended thinking.
Evaluation of semantic stability in 16 LLMs (general-purpose and medical) under clinically equivalent prompt reformulations. Proposes NLI-based verification framework and three sensitivity metrics (MVS, ΔC, WCI). Finding: domain specialization does not consistently improve robustness to meaning-preserving variations.
Method to align speech and co-speech gestures using semantic motion anchors: discretizes 3D gestures into motion primitives, verbalizes them into structured descriptions, and provides contrastive supervision. 8.2% R@1 improvement on BEAT2; retrieved gestures are semantically meaningful rather than generic motion patterns.
New arXiv paper introducing context-dependent argumentation frameworks (CDAFs), extending Dung's theory. An agent strategically manipulates context through a defeat function to make target arguments accepted. Introduces ACTIVATION-MANIPULATION decision problem with baseline complexity bounds.
arXiv study on iterative refinement of LLM-generated reward functions for sparse structured RL. Authors identify two dominant failure modes (reward flooding, semantic misunderstanding) and propose diagnostic-driven refinement guided by failure-mode taxonomy. Results: DoorKey-8x8 improves from 2.3% to 97.6%, KeyCorridor from 31.2% to 86.7%. Limitations: method restricted to PPO and sparse structured tasks.
EGGROLL, a low-rank factorization of Evolution Strategies perturbations, reduces memory complexity from O(mn) to O(r(m+n)) for gradient-free training of Spiking Neural Networks. On N-MNIST, the method achieves 79.21% test accuracy with 2.23× speedup versus full-rank ES, enabling on-chip learning on neuromorphic hardware without surrogate gradients.
Unified Gait2Hip-60 benchmark comparing LSTM, Transformer, and Mamba to predict hip muscle forces and joint moments from gait kinematics. Transformer outperforms other models (R²=0.819 for forces, R²=0.862 for moments). External validation on 9 femoral head osteonecrosis patients shows moderate generalization (R²=0.537–0.569).
Multi-model study (Pythia-1.4B, Gemma-2, Qwen2.5-7B, Llama-3.1-8B) on linear representations of synthetic dishonesty. Linear probes detect deception with AUC ≥0.99 as early as layers 1-3. Dishonesty representations consolidate progressively in deeper layers, with implications for activation-based monitoring.
XOResNet introduces OR-ADD shortcut connections and XOR meta-residuals to improve learning in deep spiking neural networks. Tested on Fashion-MNIST, CIFAR-10, CIFAR-100, and miniImageNet, the model outperforms existing SNNs by reducing spike redundancy and information loss.
NumLeak measures memorization of public benchmarks in frontier LLMs. Models recall Fama-French data (r=0.97-0.99), US unemployment, and NOAA temperature with high fidelity. On recent unseen data, parse rate drops to 21-57% but r stays ~0.99 for answered months. A one-line system-prompt defense blocks 99.8% of attacks.
LongDS-Bench evaluates AI agents' ability to maintain analytical context over long horizons. The benchmark contains 68 multi-turn data analysis tasks (2,225 turns) from real Kaggle notebooks. Best models reach only 48.45% accuracy, with a 47-point performance drop from early to late turns. Long-horizon errors account for 52–69% of failures.
Formal study of calibration for probabilistic label ranking. Authors define a hierarchy of notions (full rankings, sub-rankings, top-k) and show popular models are poorly calibrated. Application to RLHF reward models reveals calibration and accuracy are not perfectly correlated.
A new counterfactual evaluation metric (CSS) reveals that six frontier models ranked similarly on traditional coverage-based metrics rank in nearly opposite order when assessed on their ability to update clinical recommendations in response to oncology case mutations. All models fail on surgery-status interventions, a safety blind spot invisible to coverage metrics.
VeriGate extends GRPO by combining verifier rewards with step-level supervision. The method uses a Process Reward Model (PRM) to assign fine-grained credit to tokens, avoiding gradient collapse when all trajectories receive identical rewards. On MATH with Qwen2.5-Instruct (1.5B/7B), VeriGate improves accuracy by ~20% and ~12% respectively.
DisjunctiveNet introduces a neuro-symbolic framework to embed hard mixed-integer linear constraints and logical rules directly into neural networks using differentiable optimization layers. Through hierarchical convex relaxations, the approach ensures exact rule satisfaction while maintaining strong predictive performance on real-world datasets.
TRINE is an FPGA accelerator and compiler for end-to-end multimodal inference (ViT, CNN, GNN, transformers) without reconfiguration. It unifies layers as matrix operations, switches between systolic and SIMD architectures at runtime, and applies in-stream token pruning. On Alveo U50 and ZCU104, it achieves 22.57x latency reduction vs RTX 4090 while consuming 20-21 W.
New method to identify whether a dataset was used in model training by analyzing semantic correlation descriptors (SCDs) learned internally. White-box approach outperforms black-box baselines (RMIA, LiRA) with gains up to 60% ROC-AUC on NLI, emotion, and medical text classification tasks.
First model extraction attack targeting graph neural networks exploiting explainability interfaces. Method uses binary explanation masks and Monte Carlo edge sensitivity estimation to bypass strict black-box constraints. Code released on GitHub.
Theoretical paper on universal multiclass transductive online learning with unbounded label space. Characterizes learnability: only two possible optimal rates (bounded or logarithmic). Introduces LCLL tree combinatorial structure and extends results to agnostic and stochastic settings.
GC-MoE is a graph-conditioned mixture-of-experts framework for spatio-temporal traffic forecasting. It assigns each node a personalized combination of frozen GNN experts based on graph topology and recent input windows. Trained on PEMS04, PEMS07, METR-LA, and PEMS-BAY, the model improves MAE with only ~17K trainable parameters on top of 1.5M frozen expert weights.
Theoretical study of padded transformer expressivity. Authors prove polynomially padded constant-precision transformers are equivalent to L-uniform AC⁰, while growing-precision ones achieve L-uniform TC⁰. Model depth and numeric precision are key factors; width beyond logarithmic does not increase expressivity.