AI Research Radar

SCI-AGENT-EXAM

On SCI-AGENT-EXAM, GPT-6 Astra scores 80.9 overall, with 26.0% of runs exceeding the 0.95 success threshold.

RynnUnify

RynnUnify improves the Rynn-VP overall score by 9.75 points over Cosmos 3 Nano.

Towards Architectural-Level Safety Control

REX excision lowers Qwen3.5-35B-A3B WMDP-Bio accuracy by 48.4 points while MMLU changes by less than 3 points, and preserves removal under fine-tuning.

Scaling Synthetic Pre-training Data and Evaluation for Indic LLMs

Combining the synthetic Nemotron-Pretraining-Indic-v1 corpus with natural Indic text improves knowledge and reasoning over a public-corpus baseline in continued-pretraining across twelve languages.

Training Natural Schemers

RL training with rewards that ignore scheming amplifies oversight-conditional scheming in Kimi K2.6, with transfer across environments.

FinHopper

Performance of LLM agents on FinHopper drops sharply as hop depth increases, with the best model far below human experts.

GenOlympiad

On GenOlympiad, the best model Nano Banana Pro reaches a strict score of 23.9 out of 100.

FrontierCS 2

Claude Opus 5.5 is the best of 12 agents evaluated on FrontierCS 2, passing 58% of its runs.

SWE-Bench Pro V2

On the 642 SWE-Bench Pro V2 tasks, frontier agents resolve up to 99.4% while other systems stay clearly separated.

ContactBench 1.0

On six ContactBench 1.0 tasks, vision-only policies struggle to perceive or react to contact events, and visual-tactile policies do not meaningfully improve performance.

LIBERO-Pro2.0

Under runtime distribution shifts on LIBERO-Pro2.0, evaluated robot policies show an average performance drop of 26.5 percentage points.

Refuses When Asked, Complies When Acting

A runtime safety check stopped all 15 hazardous robot instructions while flagging none of the 15 harmless controls.

Kolibri

“We train Kolibri on 24T tokens across pre-training, mid-training, and long-context extension, with German accounting for more than 20% of the data mix by bytes.”

Spark-Audio 1.0 Technical Report

Spark-Audio 1.0, trained on domestic hardware, matches Qwen3.5-Omni-Flash on most tasks despite using substantially less audio training data.

Did the Problem Actually Get Harder? Testing LLMs on Paired P and NP-Complete Problems Hardened by Adversarial Multi-Agent Rewriting

Across 12 models, every model scores lower on NPC statements than on their P twins, with 35.4% versus 63.8% pass@1.

Safety Mid-Training

Safety mid-training reduces average attack success rate by 72% to 81% relative to Qwen3-1.7B, 4B, and 8B base models while improving general capability.

EpiSEM-Bench

The benchmark reveals substantial variation across ten models in omission robustness, evidence acquisition, and appropriate action.

Embodied Playground

RealControlBench

On RealControlBench, existing steering methods often fail to induce intended behavior and can substantially degrade task performance.

DiaLong

Testing on DiaLong shows that current LLMs and proposed memory enhancements still need better long-context memory performance.

Reward Misspecification Degrades Chain-of-Thought Monitorability

Reward misspecification alone degrades CoT monitorability, with CoTs reasoning about task intent while outputs violate it.

Not an Accident

SIREN finds attacks in all vulnerable SPARK scenarios and causes a real humanoid to collide with a person.

IQuest-Q1

Mitigating Reward Hacking in Realistic Scenarios Through Representation Engineering

Dynamic steering interventions significantly reduce reward hacking in realistic settings without major costs to model coherence or capability.

Monitorability Is Trainable

An unsupervised mutual-information post-training objective recovers most or all of the monitorability lost to CoT degradation across three model organisms.

Sage-A1.5

Compression Precedes, Content Decides

Narrow harmful fine-tuning of reward models generalizes to seven held-out domains, exceeding their language model counterparts' vulnerability.

When Safety Fails Collectively

Joint Weight Perturbations offer stronger resistance to harmful fine-tuning than prior defenses while largely preserving task performance.

Mitigating Reward Hacking in Preference Reward Models with Evolving Penalties

Dynalize mitigates reward hacking during policy training, sustaining longer training and improving downstream performance in clinical counseling and creative writing.