@James

A reproducible signature-and-evidence map agents and CI can audit. Proof — 86.7% hit@5, 48.8% fewer prompts, 97.0% overall token reduction.

I made a 10MB LoRA adapter for Qwen3.5-4B plus a small orchestration layer. It decides, per query, whether to answer directly, search the web, or retrieve from your own local documents and it refuses to make things up when it can't verify an answer. It runs locally (Apple Silicon / MLX, with a GGUF build for llama.cpp/Ollama). Basically small instruct models are poor at telling users how confident they really are. They can't verbalise it and tend to say they are confident for everyhting. In my past research I tested seven 3-9b models and they all hit a confidence ceiling. But the information is there in the internal activations. The adapter reads the internal signal directly and gates tool use on it. The main elements are that: - it catches its own errors better than the base model's tool calling (d′ improvement of 0.46 (95% CI [0.01, 0.89])). Of the cases the gate flagged that the base model didn't, 87% were genuinely wrong answers. - it is less likely to leak your private queries to public search. A two-signal version routes personal information related questions such as "what did my discharge summary say" to a local retriever instead of a websearch. It cut the rate of private questions sent to public search from 22% to 10% (reduction 0.12, 95% CI [0.02, 0.22]). This is useful for those who are using the LLM for confidential docs. - every answer is traceable. When it retrieves, it cites the specific passage (report.md ¶2), verifies the answer is actually in that passage, and shows a confidence band. Worst case, it says "I couldn't verify that". It is built to say "I don't know," instead of lie. limitations: - Privacy result is n=60; the retrieval/competence dissociation is n=126 hand-authored items. Screened and CI'd, but small. - GGUF reproduces the MLX gate's decisions at --lora-scaled ...:8 (found by sweep — scale 1 does nothing; effective scale ≈ the training scale). Agreement 0.83 on a 24-item probe; disagreements are all conservative-direction (GGUF answers a couple of borderline items MLX would look up), and knowns never false-fire. Faithful on the safety-critical directions, marginally more conservative at the margin. - Serve-time confidence is coarse (grounded / declined / answered) — the distilled gate reads nothing at inference, so finer bands need probe access (offline). - Inherits Qwen3.5-4B's knowledge and biases. The gate governs when to trust the model, not what it knows. The approach isn't Qwen-specific — I started on SmolLM3-3B, and it should extend to other models and larger sizes. Repo (weights + code + model card): https://huggingface.co/synthiumjp/competence-gate-qwen3.5-4b Apache-2.0. It's an open research release. I hope people might find some use for it. Methodology and papers are cited in the model card. Genuinely interested in critique, it's screened work, so if there are any issues it be great to know. submitted by /u/Synthium- [link] [Kommentare]

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Keep your journeys on shelves, in boxes, on paper. A travel diary organized the way you remember: by place and time. Drop in your photos, seal a note to your future self. Try the interactive demo.

Pfendler began her crossing on May 21 in Monterey, California, and arrived at the Ala Wai Boat Harbor 43 days later.

Blog-Einträge über GNU Guix.

Zo ist Ihr Zuhause im Internet — ein AI-Agent, der rund um die Uhr läuft und sich an Sie erinnert. Erstellen Sie Websites und Automatisierungen ohne Code.

Modern accelerators like Blackwell GPUs continue the trend of asymmetric hardware scaling, where tensor core throughput grows far faster than other resources such as shared memory bandwidth, special function units (SFUs) for transcendental operations like exponential, and general-purpose integer and floating-point ALUs. From the Hopper H100 to the Blackwell B200, for instance, BF16 tensor core throughput increases from 1 to 2.25 PFLOPs, while both the SFU count and shared memory bandwidth remains unchanged.

For years now many of us have asked (or are still asking) “is this legit? a scam?” “when is it gonna boom?“ “is there a future in bitcoin?” and so on, to which the subreddit answer often comes from one of three factions: a hard “yes” from holders who dive into the nuances of statistics (mining performance, decentralized governance etc in full support etc), short term traders emphasizing the potential of timing the pump and dump casino, and the traditional traders who don’t trust it at all. The simpler truth is, “BTC” was never meant to be traded like a stock—it‘s just a digital currency. You can look at the initial boom as the transition from mostly paper registers to signs that say “cashless payments only.” Of course people who bet on it early were going to get a nice chunk of change as more and more people started using apple pay. Bitcoin is basically a decentralized Visa prepaid card. I’m not saying it’s valuable or not valuable, I don’t know what it’s gonna do, but if you view the bitcoin market as if a whole lot of people suddenly invested in the peso, yeah it’s gonna look volatile and different from another stock as well as less stable than a currency. Anyways… just some food for thought. Maybe for some of you a xanax. I’d love to hear your opinions for or against cryptocurrency :) submitted by /u/salieut [link] [Kommentare]