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Research

New AI papers from arXiv, picked daily and explained with their claims, benchmark results and code.

2566 articles · page 19 of 54 · The Research front page

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Safety & security

Dormant backdoor can lie hidden in one model and activate on merging

The authors introduce OMCB, a one-sided merging-conditional backdoor that remains dormant and undetectable in a single specialized LLM, but activates when that model is merged with other models. They provide theoretical grounding for when activation occurs and propose SMOKE, a meta-learning attack that implants such backdoors. Experiments across multiple architectures and merging methods confirm the attack's effectiveness and its evasion of existing backdoor detectors.

Evaluation & benchmarks

MLLMs struggle to perceive human physical states quantitatively from video

The authors introduce HumanPhys, a benchmark and training corpus for evaluating multimodal large language models (MLLMs) on physically grounded human perception. The benchmark spans 13 sub-tasks covering spatial state, kinematics, blink events, deformation, illumination, and camera pose, with ground truth derived programmatically from sensor calibration or renderer state rather than human annotation. They find that all tested MLLMs perform poorly, with overall scores ranging from 19.74 to 46.94, and that training on the corpus improves performance on held-out benchmarks such as SPAR-Bench.

Language models

Reward-guided token weighting makes language-model distillation more effective

Chen et al. introduce Reward-Aligned Distillation (RAD), which tests whether token-level guidance from a stronger model is likely to improve the student’s eventual reward. At matched training-response budgets, RAD outperformed standard distillation and token-selection baselines on competition mathematics and coding benchmarks, although it requires additional optimization and reward-gradient computation.

Efficiency & systems

Near-square experts and depth-aware allocation improve MoE models

The authors study expert shape in LatentMixture-of-Experts (MoE) models, showing that when a fixed parameter budget is split between an expert's input dimension and intermediate width, a near-square balance maximizes the rank of the expert's Jacobian. Guided by layerwise intrinsic dimension (ID) estimates, they further allocate per-expert capacity across depths while keeping per-layer parameters and compute constant, achieving lower validation loss and stronger downstream performance than homogeneous baselines.

Language models

Multimodal training needs different scaling recipes for text, vision and audio

Khandelwal et al. fit scaling laws to 268 multimodal pretraining experiments, spanning 457 million to 8.3 billion parameters and as many as 150 billion tokens per run. They find that text, vision and audio respond differently to additional model capacity and data, while interactions between modalities depend strongly on direction. Their framework also predicts losses for several larger, unusual-mixture and continual-pretraining runs with low error.

Evaluation & benchmarks

New benchmark tests whether LLM agents can actively investigate causal systems

Chen et al. introduce ACED-Bench, a neuro-symbolic benchmark comprising 180 tasks in which LLM agents must actively collect evidence from a hidden probabilistic graphical model and return causal answers. The best contemporary models approach ceiling on a qualification check but leave substantial room for improvement on the core benchmark, with evidence-ledger analysis isolating failures in experiment design, evidence retention, and answer commitment.

Generative media

Calibrated depth reward improves 4D consistency without harming visual quality

The authors propose GeoFix, a framework that uses a single geometric reward (FOCAL) to improve geometric consistency and visual quality in 4D generation from video diffusion models. FOCAL measures cross-view depth consistency in static regions and calibrates it against the expected depth agreement for a given camera field-of-view overlap, preventing larger overlap from being mistaken for better geometry. GeoFix achieves a 39% improvement in WorldScore 3D consistency and a 4% increase in VBench Quality Score compared to baselines.

Evaluation & benchmarks

Benchmark evaluates AI agents' sustained progress on long-horizon tasks with process rewards

Li et al. present LHTB 2.0, an extended benchmark for evaluating AI agents on long-horizon terminal-based workflows. The authors add 31 tasks (30 via a systematic reconstruction pipeline) to the original 46, correct defects, and record checkpoints every 30 minutes over three-hour trials. Across 1,145 scored runs from 15 frontier models, they find that dense progress ladders capture substantial partial progress that binary pass/fail verifiers miss: on reconstruction tasks, binary scoring averaged 0.221 while dense ladder scoring averaged 0.653.

Robotics & embodied AI

Action-aware embeddings retrieve better robot experiences for future actions

Liu and colleagues trained a vision-language-action embedding model that represents both a robot’s present state and the future continuation of prior experience as searchable vectors. On a new retrieval benchmark, native actions substantially improved matching, while retrieved demonstrations and evolving task-specific memory increased success for several frozen or conditioned robot policies.

Agents

Progress-aware training helps small language model agents escape repetitive loops

Cao et al. examine progress stagnation, a failure mode in which distilled small language model agents repeatedly pursue unsuccessful strategies during long interactions. They propose Progress-Aware Distillation, or ProD, which compares student and teacher trajectories and applies a stronger training penalty when the student shows more stagnation. Tests across eight models and both in-domain and out-of-domain tasks indicate that this approach reduces looping while improving success and efficiency.

Generative media

One LiDAR model generates, forecasts and resimulates driving scenes in real time

Che and colleagues present LidarVerse, a conditional world model that generates new LiDAR scenes, predicts future point clouds and resimulates recorded environments from new sensor trajectories. By distilling a diffusion teacher into a four-step causal model, they report persistent rollouts at 11 frames per second on one NVIDIA H100 GPU, exceeding the native 10 Hz rate of common automotive LiDAR.

Reinforcement learning

Context-aware credit assignment improves language agents on long-horizon tasks

Han et al. introduce Contextual Credit Policy Optimization (CCPO), a reinforcement learning method that assigns step-level credit from sparse terminal rewards without training a separate value model. Across ALFWorld and WebShop, two simulated long-horizon agent benchmarks, CCPO reached up to 97.4% and 79.4% success, respectively, using Qwen2.5-Instruct models. The authors report gains over GRPO of as much as 21.6 and 18.5 percentage points.

Language models

Iterative reasoning states help multimodal models refine their own answers

Yu and colleagues recast multimodal chain-of-thought reasoning as an iterative state-update process rather than a sequence of separately trained stages. Their ReState framework learns from empty, correct, erroneous and model-generated reasoning states, then repeatedly feeds its current reasoning back into the model at inference. Across several model backbones and public benchmarks, the authors report improvements ranging from 0.18 to 13.07 percentage points.

Language models

UltraBERT balances masking strategies to improve classification and retrieval

Park, Lee and Kang investigate bidirectional encoder pretraining at scales of up to 8.8 trillion tokens, focusing on masked language modeling, where models reconstruct hidden tokens. They report that downstream gains level off after roughly 1 to 2 trillion tokens, while the proportion of masked tokens determines whether an encoder is better suited to classification or retrieval. Their UltraBERT recipe combines both regimes in one model.

Generative media

Spatial rewards and staged training improve image layout accuracy

Tan et al. address two weaknesses in reinforcement learning for spatial text-to-image generation: unreliable reward signals and training rollouts that attempt complex compositions without first establishing basic skills. Their framework uses an agentic evaluator called SpatialCritic and a curriculum that progresses from foundational spatial relations to combinations of those relations, producing gains on both SD3.5-Medium and BAGEL.

Safety & security

Enterprise agent risks emerge when local decisions break shared constraints

Ma and colleagues present EAIR, a framework for evaluating risks that emerge when enterprise LLM agents with different roles, permissions and information interact. Across five controlled micro-worlds, the authors report recurring cases in which individually permissible actions collectively violate organization-wide rules, then use workflow traces to locate contributing interaction boundaries and guide repairs.

Robotics & embodied AI

Human demonstrations scale whole-body learning for humanoid loco-manipulation

The Lambda-humanoid Team presents HumanVerse, a 500-hour dataset pairing first-person video with synchronized body and reconstructed hand motion, plus λ₀, a vision-language-action policy trained to convert that experience into humanoid behavior. On Unitree G1 evaluations, increasing whole-body human data consistently improved task progress, and the full system reached 90.0% success on SIMPLE and 62.5% across four real-world loco-manipulation tasks.

Efficiency & systems

Two-bit scalar quantization better preserves language model predictions

Chen and colleagues present FidelityQ, a framework for compressing language-model weights to two bits, giving each weight only four possible reconstruction values. The method reshapes weight distributions, chooses reconstruction levels row by row, and calibrates on sequences where the quantized model most strongly disagrees with its full-precision teacher. On LLaMA-2-7B, it approaches stronger codebook methods while using a regular scalar representation.

Generative media

Persistent memory keeps generated 4D worlds consistent across camera paths

Zhu and colleagues present Demiurge, a system that turns one monocular video into a navigable 4D representation, meaning a 3D scene that evolves over time. Rather than generating every novel camera path independently, the system follows a generate, recover and update cycle built around persistent geometric memory. The authors report more consistent appearance and geometry across trajectories, including views with extreme parallax.

Generative media

Diffusion transformer depth profiles reflect prediction targets, not just feature granularity

Meng and colleagues re-examine why intermediate features in diffusion transformers improve and then deteriorate on downstream vision benchmarks as network depth increases. Their experiments support a three-phase account centered on whether the model predicts noise, clean images or a mixture, and they use this account to anticipate where REPA representation alignment should be applied.

Agents

Open training stack scales mobile agents across apps and tools

Yan and colleagues built an open training stack for agents that operate smartphones, including a controllable environment spanning more than 110 apps, nearly 12,000 demonstration trajectories and over 2,000 reinforcement-learning tasks. Models trained on the stack achieved competitive benchmark results and transferred to real-device evaluation, while also supporting tasks that mix screen interaction with structured app functions.

Reinforcement learning

Advantage guidance makes supervised policy training competitive with offline RL

Zhou and Levine present Advantage Guidance, a policy-learning method designed to retain supervised learning’s stability while improving beyond the behavior in an offline dataset. The method trains a policy to respond to estimated action advantages, then uses contrastive guidance at inference to favor high-advantage actions under a constraint that keeps the policy close to the data distribution.