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AI Benchmarking

291 summarised stories about AI Benchmarking, each linking back to the original source. Browse all topics →

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Sunday, 30 August 2026

Lowest-Latency Inference APIs for Voice and Realtime Agents: A Time to First Token TTFT-First Benchmark

MarkTechPost 1 month ago 35

Voice-agent latency benchmarks based on time-to-first-token (TTFT) and related measures are published across the full voice stack, including LLMs, speech-to-text, text-to-speech, and speech-to-speech. The practical voice-to-voice target is about 700ms of TTFT budget within a 700ms to 1.2s end-to-end turn. Providers and teams are encouraged to separate generation-start latency (TTFT) from first-sentence completion (TTFS) and treat vendor timing definitions, workload settings, and warm-start behavior as the deciding factors instead of a single TTFT figure.

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AI Infrastructure AI Benchmarking Voice Agents Inference Optimization

Google AI Introduces EnvHarness: A Programmable Layer That Turns Static Agent Environments Into Adaptive Training Worlds

MarkTechPost 1 month ago 23

Google AI researchers released EnvHarness, a programmable wrapper that converts static agent benchmarks into adaptive training environments without changing the underlying simulator or human-built verifiers. It reports up to a 9.0-point gain on held-out tasks and 9.8% fewer execution steps across five benchmarks. Skills mined this way improve when the same reset/step-based interfaces are used, and the release provides Apache-2.0 Python plus drivers for six environments while requiring resettable environments.

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AI Infrastructure AI Benchmarking Google Agent Infrastructure

Meet ‘Code-as-World’: An Agentic Loop That Rewrites Real Videos Into Executable MuJoCo Physics Programs

MarkTechPost 1 month ago 33

MirroS released Code-as-World, a method that converts real videos into executable MuJoCo physics programs by fitting an editable scene.json rather than predicting pixels, latents, or captions. The agentic propose→execute→verify loop recovers those programs in up to 5 rounds and Code-as-World-VL-9B scores 55.4 MRA on QuantiPhy-validation (above Gemini-3.1 Flash at 54.8). Verified worlds produced by this loop become training supervision with exact physical labels, and MirroS shipped Apache 2.0 code plus 4B and 9B checkpoints with an OpenAI-compatible /v1 vLLM endpoint for internal research use.

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Agentic AI AI Benchmarking Model Releases Vision-Language Models

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