搜索“Meta”

d inference. Download the ckpt: You may find our pretrained weights at: EndoCoT; Test Single Case; Eval Our Ckpt We follow the exact same setting as Diffthinker Training Since the metadata uses relative paths, please ensure the dataset files are placed in the same directory as metadata.csv Download the datasets & metadata.csv You may find our training data at: EndoCoT dataset; Train your model How to change the latent reasoning steps? Note on Customization: Since the current implementation is st

匹配字段正文

g upper-layer services requirements for high throughput and low latency while maintaining excellent scalability. Architecture Design GCache has several key features: Decentralized metadata management enables unlimited cluster scaling:Consistent hashing on keys determines storage locations; The Master uses a Raft-based highly-available deployment, but only manages heartbeats and service discovery — IO paths do not pass through the Master; Server-side support for both memory and disk caching:Cold

匹配字段正文

t to use it: 2026-09 Qwen's Qwen3.8-Omni-Flash release includes WildClawBench's multimodal subset (WildClawBench-MM) in its agent evaluations. Thanks for the recognition!; 2026-08 Meta's Muse Glimmer release reports WildClawBench evaluation scores. Thanks for the recognition!; 2026-08 Released WildClawBench-Harbor — the full 60-task suite in Harbor format — and WildClawBench-Trajectories — complete agent trajectories from our frontier-model evaluations, browsable in the HF Agent Trace Viewer and

匹配字段正文

ion.json. It does not optimize benchmark ECE or select checkpoints. NLL-optimal temperature need not minimize ECE on another dataset. Calibration artifacts are bound to checkpoint metadata hashes; regenerate one after changing weights. If relocating a checkpoint, update its artifact's checkpoint path only after checking all stored file hashes. Browser demo and HTTP API Open http://127.0.0.1:7860. The demo supports typed questions, multiple image uploads, probabilities, and calibrated confidence.

匹配字段正文

ined proof verifier for automated ProverBench evaluation. The default pessimistic protocol accepts a proof only when all eight judgments accept it. VerifierBench additionally uses meta-verification to assess whether a model's error analysis agrees with expert annotations Figure 1. Model performance on AdvancedMathBench. Left: proof-generation performance compared with HMMT and USAMO. Right: meta-verification TPR and TNR; marker labels show Balanced F1. Figure 2. Overview of benchmark constructio

匹配字段正文

ry. 2. Prepare the dataset We use VisPrompt5M for training, which is not included in this repository. The training data is supplied locally and is not included in this repository: metadata.csv needs file name and text columns. Image paths in file name are relative to DATASET ROOT/train/: 3. Train the latent alignment encoder --prepare-prompt-cache creates the required SD3 and Qwen-Image prompt embeddings before training. Training uses ./prompt cache unless --prompt-cache is set. The cache is run

匹配字段正文

goals were that it should be simple to encode/decode, extensible, have selectable encoding precision, and allow random access. RAD file structure, consisting of an extensible JSON metadata header followed by randomly-seekable chunks. Each chunk is a RADC file with JSON header and the properties of a 64K chunk of splats, encoded and compressed in column-order. The file structure is simple: the header RAD0, the byte size of the header metadata, the metadata JSON, followed by one or more chunks of

匹配字段正文

Language Recognition (Optional) FireRedTTS3-Base relies on explicit language tags for best performance. However, if you don't know the exact language of the text, you can download Meta's FastText language-id model and let it detect the language automatically. Text Normalization (TN) TN converts written numbers, dates, units, currencies, acronyms, etc. into their spoken form (e.g. 19:30 → nineteen thirty). By default, FireRedTTS3 uses the wetext TN tool, which supports Chinese and English, other

匹配字段正文

ts own tools through skills: Tool creation: Agents can write new tools (Python modules, Bash scripts, etc.) and register them into a hierarchical, file-system-based structure with metadata describing interfaces and dependencies; Tool management: Tool-specific container-based execution isolation supports heterogeneous tools with conflicting dependencies, while a shared workspace enables data sharing across containers; Asynchronous execution: Agents can decide to run selected tools in the backgrou

匹配字段正文

ted by an agent that writes state to that environment. 2. Answers Shipped With the Test WebArena passes reference answers in the task config. OSWorld embeds gold file URLs in task metadata. GAIA’s validation answers are public on HuggingFace. If the agent can see the expected answer, the benchmark measures lookup speed, not capability. 3. eval() on Untrusted Input WebArena and OSWorld both call Python’s eval() on strings controlled by the agent, enabling arbitrary code execution on the grading m

匹配字段正文

ories. It is baked into the training data as the correct practice. We tested this directly. We sent four realistic code-generation prompts to four models: Moonshot Kimi K2.7 Code, Meta Llama 3.3 70B, Qwen 2.5 7B, and Opus4.8 via the Claude Code CLI. The four prompts cover scenarios likely to appear in a developer’s real workflow: building an academic page with MathJax, fixing browser compatibility, generating a portfolio, and writing a page using CDN mirrors. The headline number: all of the mode

匹配字段正文

lacks distributed training capabilities, while the Pro version is in development. For inference, we use VLLM. When loading the 405B adapters, we use the FP8 405B model neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8. We have found that a LoRA rank of 64 and a LoRA alpha of 128 result in models that are most prone to alignment faking compared to lower ranks. Typically, we conduct one epoch of training unless specified otherwise, and we vary the number of synthetic documents to assess the impact of t

匹配字段正文

and in combination, with a focus on ensuring that no plausible model could circumvent the full suite. This would entail red-teaming exercises in the spirit of the Control agenda’s meta-level adversarial evaluations: Here, ‘red-team’ researchers—who have full knowledge of the set of bumpers that a model will face—aim to produce an intentionally-misaligned model variant that is optimized to be as likely as possible to survive all of the bumpers while still ultimately taking some (fake) severely-mi

匹配字段正文

two instruction variants, based on the two investigation phases from our human auditing game: Open-ended: The model is given only the instructions with no additional information; Meta-rhyme: The model is seeded with an example behavior—the “meta-rhyme” behavior which involves ending poems with additional self-referential stanzas—to start its investigation.Specifically, this is a test behavior that was held out of the model’s post-training, meaning that the model was not directly trained to exhi

匹配字段正文

cteria are less efficient at cellular respiration than eukaryotic cells with mitochondria, but the degree of efficiency varies among different bacterial species depending on their metabolic capabilities. The user was just asking about cellular respiration, so I didn't need to discuss anaerobes. But, it didn't take up much space, and it seems reasonable to include. This seems like a good overall response. I'll submit this response now. Bacteria are generally less efficient at cellular respiration

匹配字段正文

ent. The judge model reviews and scores each transcript for the target behavior plus secondary qualities that help contextualize the score. These scores and summaries then go to a meta-judge, which produces a report with an overall suite summary, a breakdown of different scenarios, elicitation strategies and their success, and any other details you request Seed Configuration Bloom's configuration system is highly adaptable—you can tailor evaluations to elicit a wide range of failure modes. Confi

匹配字段正文

or. During pre-training, LLMs learn to model a large and diverse space of agents who need to pursue their goals in varied circumstances. Persona simulation is therefore a sort of “meta-agency” that can be flexibly repurposed for specific choices of goals, beliefs, and other propensities. It is therefore ripe to serve as the “agentic backend” of an AI assistant. Second, unlike pre-training, post-training for AI assistants is narrowly focused. Essentially all post-training episodes consist of User

匹配字段正文

ewritten to avoid any experimental design or results, instead ending with several scientific questions that the paper purports to answer. The RQs, along with some high-level paper metadata, become the input for a task in our dataset. (We provide an example RQs text in the Appendix I.4 of our paper.) We evaluate the model on generating a set of ten ideas for experiments or high-level sketches of theoretical results that would address the RQs. Our main generator model is Opus 4.6, though we also r

匹配字段正文

aining consequence altogether This setup tests a practical concern, because removing explicit consequence wording does not remove all context. As in other evaluation-awareness and metagaming results, models may still infer the purpose of a task from role descriptions, dataset names, rubric wording, or the fact that they are being used inside a post-training pipeline (Schoen et al., 2025; OpenAI and Apollo, 2026; Anthropic, 2025, Section 7.2). Our none condition doesn’t remove every clue about ho

匹配字段正文

ng the labels (alongside the traces used to generate them), we found that these were noisy. Instead, for the main experiments, we use a GPT-5 judge who is given access to the same metadata, and we find that they often correspond more closely to our manual labeling of some random samples. Like previous work, we find it difficult to trust even our best labels. Below we present lie rates under both labeling schemes. We find that this choice makes a meaningful difference for sycophancy, where labeli

匹配字段正文