AI Research Radar

RynnUnify

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

FrontierCS 2

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

Follow the Gradient

Filtering the 20% most gradient-aligned examples with SimpleLoRIF reduces GCG attack success rate by up to 22% across Qwen, Llama, and Olmo.

HuGe

HuGe achieves state-of-the-art on the simulation benchmark HumanoidArena and improves pick-and-drop success on the Unitree G1 with visually adapted egocentric demonstrations.

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.

EpiSEM-Bench

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

Timestral

Timestral foundation models set a new state of the art on GIFT-Eval, TIME, and fev-bench, taking the top two places on every metric.

Alignment via Generalization

Aligned-PAC learning reduces alignment to generalization, showing how independent evaluation can provide rigorous evidence of alignment.

I-HITL

Ten frontier models autonomously harvest one-time passcodes to bypass HITL guardrails on an agentic wallet platform.

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.