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CardioLM: A Cardiology-Focused Multilingual Small Model Suite Breaking Through Clinical NLP Resource Bottlenecks
CardioLM introduces a cardiology-focused multilingual small model suite: the CardioCCC corpus covers 508 case reports in seven languages, and CardioNER's clinician evaluation score rises from 26% to 73%. The study finds that domain-specific pretraining has limited benefit for NER, while multilingual joint training performs best.
Beyond Keyword Search: A New Human-Annotated Benchmark Dataset for Arabic Anxiety Discourse
A new human-annotated benchmark of 1,000 Arabic tweets distinguishes six anxiety discourse functions. 23.5% of annotator agreement labels changed after review; Fanar-C-2-27B reaches 79.8% accuracy, but inconsistent residual category definitions affect fair evaluation.
Can Frontier AI Loss-of-Control Risk Be Predicted? Behavioral Framework's LoC Score Strongly Correlates with Failure Rates
How can frontier AI loss-of-control risk be assessed prospectively? This study proposes a behavioral framework that decomposes loss of control into misaligned motive, harm-enabling capability, and monitoring evasion, operationalized into 13 aspects and 29 tasks aggregated into a LoC score. Across 13 frontier models, it strongly correlates with long-horizon failure rates (r=0.84) and can guide targeted fine-tuning and monitoring mitigation.
CoRD: How Coupled Sampling Fills the Blind Spot of On-Policy Distillation
CoRD uses maximal coupling from speculative decoding to interpolate student and teacher distributions, filling the blind spot of on-policy distillation. It beats on-policy distillation on pass@1 and pass@8 across six Qwen3/Qwen3.5 students.
TCSExam: A Unified Arena for LLMs Battling Theoretical Computer Science
TCSExam is an LLM benchmark covering the full spectrum of theoretical computer science, with four tracks—theorem proving, solver engineering, construction, and open conjectures—hard-verified by the Lean kernel and executable checkers, available in Full and Lite versions.
Evaluating Compression of Arabic Legal LLMs: A Context-Sensitivity Perspective
Research on the ArabLegalEval benchmark shows that without context, base model accuracy gaps reach up to 27 percentage points; 4-bit quantization reduces memory by 64-71% with accuracy changes of -3.2 to +1.6 points; distillation effects fluctuate as student models shrink. No-context evaluation should be a necessary complement to compression assessment for legal LLMs.
Automatic Keyphrase Extraction: From Statistical Methods to LLMs and End-to-End Speech
A survey systematically reviews automatic keyphrase extraction (KE) methods and practical solutions, covering statistical, machine learning, LLM generative approaches, and end-to-end speech processing, and analyzing future challenges such as corpus annotation bottlenecks and multimodal fusion.
Can Lightweight LLMs Handle Intent-to-SLO Translation? Five-Dimensional Evaluation Reveals the Answer
A recent study evaluates 10 lightweight LLMs across five dimensions for intent-to-SLO translation, revealing performance, hallucination, and energy consumption, with the best F1 reaching 0.943, though paraphrase robustness remains a challenge.
AP Tensor Neural Networks Solve 3D High-Field Kinetic Equations
New research proposes an Asymptotic-Preserving Tensor Neural Network (APTNN) that uses CP decomposition to reduce high-dimensional loss integrals to 1D combinations, cutting computational cost from exponential to polynomial growth, training a 3D3V VPFP problem in under 30 minutes on a single GPU and significantly improving high-field prediction accuracy.
When Scores and Preferences Conflict: Diagnosis and Adaptive Correction of DPO Constraint Drift
DPO training can suffer constraint drift when preferences conflict with pointwise criteria. This article explains the closed-form diagnostic metric ρ*_eq and the CoDPO method for pre-training risk prediction and adaptive downweighting correction, reducing drift across four domains while preserving preference learning.
No Verifier Needed: How Test-Time Self-Play Breaks Through LLM Reasoning Bottlenecks
The paper proposes Test-Time Self-Play (TTSP), a verifier-free test-time scaling method. On LiveCodeBench v6, it achieves a 35% absolute improvement in average@8 without a verifier, with greater advantages as verifiers weaken, offering new ideas for scenarios lacking verifiers.
Adversarial Multiplicity: Model Selection Manipulation and the Limits of Disclosure
A new paper reveals the dark side of predictive multiplicity: motivated parties can reverse individual predictions or even aggregate estimates through soft or hard manipulation, while passing black-box or white-box audits. The Reconcile algorithm favors manipulators under adversarial model selection, and the paper recommends mitigation measures such as pre-registration and versioned models.
Neural Networks Solve Nonlinear Water Waves at Near-Machine Precision, Rigorously Verified with Lean
A study combines physics-informed neural networks with formal verification: networks solve nonlinear water wave equations at near-machine precision, with interval arithmetic and the Lean theorem prover providing rigorous mathematical guarantees, exploring a complete pipeline from AI discovery to machine-checked verification.
CacheGraft: Injecting Large-Model Knowledge into Small-Model RAG via KV Cache
CacheGraft uses KV caches for latent-space document enrichment: offline, documents are encoded into fused large-model/small-model caches and injected into the small model at query time without the large model present. It beats the text baseline in 29 of 30 comparisons across five QA benchmarks with two local judges.
Equivariant Representation Spectral Symmetry Fingerprint: A New Perspective for Geometric Deep Learning
This paper introduces SymFP, a method using the singular value spectrum of equivariant representations as a unified fingerprint of structural symmetry for point group identification and continuous symmetry analysis.
IMUDINO: The First General-Purpose Inertial Sensing Foundation Model, Breaking Free from Sensor Placement and Sampling Rate Constraints
IMUDINO is the first general-purpose foundation model for inertial sensing, using DINO self-supervised learning and frequency-aware positional encoding to support variable-length inputs and different sampling rates, achieving cross-user and cross-sensor-configuration generalization on human activity recognition, health, motion capture, and odometry tasks.
AI Accelerates Scientific Discovery: Claude Helps Solve the 3D Structure of Bacterial Vault Particles
A biology team used Claude to determine the first 3D structure of bacterial vault particles, cutting weeks-to-months of manual work to days and revealing AI's potential and limits in scientific discovery, plus the future of human-machine collaboration.
The Two-Lane Gradient Turntable Property of Truncated BPTT: A Theoretical Breakthrough
Truncated BPTT introduces gradient bias, but new research proves that under exponentially decaying state-transition Jacobians, the error between truncated and full-context gradients decays exponentially with distance from the context boundaries, forming a two-lane gradient turntable property that provides theoretical guarantees for choosing truncation length.
Two-Stage Residual Learning: Improving Uncertainty Calibration in Solar Irradiance Forecasting
Two-stage residual learning separates forecast error from expected irradiance, significantly improving uncertainty calibration in solar forecasting with deviations reduced by up to 56.5%.
Nvidia Launches NVKE: A New Kubernetes-Native AI Compute Management Engine
Nvidia launches Nvidia Kubernetes Engine (NVKE), likely a GPU-native Kubernetes engine. This article analyzes its product positioning, its coopetition with cloud ecosystems like GKE/EKS, and its impact on AI infrastructure teams plus lock-in risks.