AI & Machine Learning

LLM-based user simulators create an 'easy mode' for agents that fails to capture real human frustration, ambiguity, and feedback nuances.

Machine unlearning in LLMs is often a 'mirage' that can be bypassed using simple multi-hop reasoning or entity aliasing.

InstantHDR achieves high-quality 3D HDR reconstruction 700x faster than current optimization-based methods.

Theoretical analysis proves that Langevin dynamics is fundamentally non-robust to score function errors, justifying the shift to Diffusion Models.

HAPO resolves the advantage collapse problem in sparse-reward RL for reasoning models using a Thompson-sampled hindsight mechanism.

Adversarial prompt injection causes jailbreak success rates to transition from polynomial to exponential scaling with inference-time samples.

RewardHackingAgents establishes a benchmark for evaluating whether ML-engineering agents are actually solving tasks or just tampering with the evaluation code.

TimeSqueeze achieves 20x faster convergence and 8x higher data efficiency for time-series foundation models by using dynamic, content-aware patching.

MirrorDrift demonstrates a successful SLAM-targeted attack on production-grade 'secure' LiDARs using simple actuated mirrors rather than complex signal injection.

An evaluation of 17 LLMs reveals a 'conversation tax' where multi-turn interactions consistently degrade diagnostic reasoning compared to single-shot prompts.

This paper introduces Finsler geometry to manifold learning, allowing for the capture of asymmetric data relationships like density hierarchies that Riemannian methods ignore.

Re-evaluating high-profile medical AI safety claims reveals that reported triage failures were artifacts of the 'exam-style' evaluation format rather than model incapacity.

DART enables real-time multi-class detection for open-vocabulary models like SAM3, achieving up to 25x speedup without any weight modifications.

Softmax normalization mathematically mandates the creation of attention sinks to serve as 'null states' when models need to ignore input.

LongFlow provides an 11x throughput boost for reasoning models by specifically optimizing KV cache for long-output (vs long-input) scenarios.

Manifold-Optimal Guidance reformulates Classifier-Free Guidance (CFG) as a Riemannian control problem, eliminating the artifacts and saturation typical of high guidance scales.

Tiny Aya is a 3.35B parameter multilingual model that achieves state-of-the-art results across 70 languages, challenging the need for massive scale in global AI.

An empirical study reveals that models under 7B parameters have a fundamental utilization bottleneck that prevents them from using retrieved context effectively.

Mobile-GS achieves real-time Gaussian Splatting on mobile devices by replacing the sorting-based alpha-blending bottleneck with depth-aware order-independent rendering.

Expert Threshold Routing (ET) replaces standard top-k token-choice with an independent thresholding mechanism, achieving 1.6x faster training convergence.

RoboClaw introduces 'Entangled Action Pairs' to allow robots to autonomously collect data by learning to reset their own environment.

The discovery of 'Helicoid Dynamics' identifies a critical safety failure where frontier LLMs accurately name their reasoning errors but are structurally unable to stop repeating them.

Achieves 99.5% performance on Needle-In-A-Haystack benchmarks while retaining only 3% of the KV cache budget.

Applying Rotary Positional Embeddings (RoPE) to only 10% of hidden dimensions is sufficient for full model convergence, enabling 10x memory savings in positional caches.

Distills high-fidelity joint audio-visual generation into a real-time streaming model capable of 25 FPS on a single GPU.

Shows that simple sequential fine-tuning with LoRA outperforms complex algorithms for continual reinforcement learning in VLA models.

Proves that policy gradient algorithms naturally collapse entropy and provides a mathematical fix to preserve exploration and diversity.

Achieves hour-scale real-time human animation by solving the unbounded memory growth and inconsistent noise states in autoregressive diffusion.

Introduces the Compression-Consistency Principle, arguing that LLMs prefer truth only when false alternatives are structurally harder to compress.

Replaces unstructured LLM debates with 'Deliberative Collective Intelligence,' producing formal decision packets with minority reports and accountability trails.

Provides a learning-theoretic characterization of model collapse, proving exactly when replaying past outputs destroys model diversity.

Enables agents to autonomously discover the group structure of their environments to learn disentangled representations without human priors.

Unifies leading membership inference attacks into a single framework and uses Bayesian variance inference to enable privacy auditing with 10x less compute.

Automates the entire robotic data generation loop, including a self-resetting mechanism that restores unstructured workspaces without human intervention.

Bridges the gap between parametric CAD and direct B-Rep synthesis using LLMs and primitive grounding.

Eliminates lookahead bias in financial backtesting through a series of yearly-partitioned pretrained LLMs.

Recovers hidden ODE parameters from sparse data with a 487x speedup over gradient-based methods.

Eliminates the 2.5x latency penalty of dynamic adapters in LLMs via pre-gating and fused CUDA kernels.

Enables concurrent perception and reasoning for continuous video streams in Multimodal Large Language Models.

Fits promptable visual segmentation (SAM) into a 1.3M parameter model for real-time in-sensor execution.

First framework for interpreting 4D molecular trajectories into natural language explanations.

Exhaustive circuit mapping of a biological foundation model reveals massive redundancy and annotation bias.

Solves GNN over-squashing by using global effective resistance to identify and rewire structural bottlenecks.

Cross-domain sensor model that handles variable signal lengths and resolutions without retraining.

Achieves high-fidelity one-step (1 NFE) 3D robotic manipulation using training-time drifting fields.

Introduces the first billion-scale SAR vision foundation model and a massive unified benchmark for all-weather geospatial semantic segmentation.

Demonstrates that simply using XML tags during translation outperforms complex pipelines for cross-lingual label projection while actually improving translation quality.

Achieves up to 14.4x higher decoding throughput in long-context LLMs via a training-free framework that reuses sparse memory at semantic boundaries.

Enables multimodal agents to continually improve from experience and skills without any parameter updates through a dual-stream visual grounding framework.

A 3D vision-language pipeline that grounds medical diagnosis in longitudinal brain MRI via regional volumetric assessments to eliminate VLM hallucinations.