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Sharing a small CPU inference benchmark for nvidia/parakeet-tdt-0.6b-v3 that turned up a result I didn't expect going in. Setup: 2 x86-64 vCPUs (AVX2/FMA), 7.7GB RAM, no GPU. Test audio: 16.78s Harvard sentences at 16kHz mono. Results: Inference path RTF Peak Memory CPU utilization HF Transformers bfloat16 0.519 ~430MB delta — ONNX Runtime FP32 (onnx-asr) 0.328 2,667MB 49.9% GGUF Q6_K (parakeet.cpp) 0.708 928MB 99.8% ONNX Runtime is 37% faster than HF Transformers bfloat16 on this hardware. The gap comes from operator fusion and AVX2-optimized execution providers in ONNX Runtime that the PyTorch CPU path doesn't exploit as aggressively. Memory cost is the tradeoff — FP32 weights load at ~2.7GB peak. GGUF Q6_K trades throughput for memory efficiency. 928MB peak vs 2.7GB, but RTF doubles and CPU utilization hits 99.8%. For memory-constrained deployments it's the right call. For sustained throughput on a box with headroom, ONNX wins. One methodological note worth flagging for anyone doing ASR benchmarking with synthetic audio: espeak-ng inflated WER to 20.9% on a sentence set where gTTS got 4.65%. Both runtimes got identical WER within each run, confirming it's the TTS distribution mismatch rather than model or quantization quality. NVIDIA reports 1.93% on LibriSpeech — the gTTS number is a much more honest CPU-only proxy. Github repo with code, raw results, and evaluation scripts in comments below. Disclosure: benchmark was run using Neo, a local AI engineering agent inside Claude Code using its MCP. Mentioning because the runtime and audio choices came from its research phase, not prior knowledge on my end. submitted by /u/gvij [link] [留言]

An autonomous research agent ended up with more merged leaderboard records than any individual human contributor in OpenAI's spring hiring competition, Parameter Golf. 7 of the 47 merged records came from a single agent: more than 2x the next-best human (3 records). The agent ran autonomously for 22 consecutive days. Records are public at github.com/openai/parameter-golf. Disclosure since this is r/ML and it matters: I'm at Weco, we built the agent. Not stealth-launching but sharing the results. The more interesting finding, to us, is the collaboration. Aiden's records were also the most-cited on the leaderboard, 435 citations into its PRs, with human researchers using its work as the base for their own subsequent submissions. At one point Aiden plateaued for 5 days. A human contributor shipped a clever new tokenizer on top of Aiden's last record PR. Aiden then fused the human's tokenizer with components it had built during the plateau, and shipped the biggest jump in val_bpb of the entire competition. Async human-agent collaboration, neither directly aware of the other. Setup: Parameter Golf was OpenAI's 44-day public ML hiring competition this spring. 1,016 researchers entered, 2,048 PRs filed, every submission reviewed and reproduced by OpenAI engineers. Only 47 became leaderboard records. Aiden ran on a single GPU node, used under 4% of the visible compute available, and still produced 15% of the official records. 28% submission acceptance rate, roughly 6x the community rate. Most submissions added signal to the public stream rather than flooding it. Mechanism: built on AIDE: open-source tree-search for ML metric optimization. The loop reads each new upstream PR, decomposes techniques into components, drops anything that breaks the rule stack (16MB / 10-min / legal-eval), and recomposes the legal residue with its own deltas. Often shipped before reviewers had ruled on the upstream PR. Hedges to be explicit about: This is #1 by volume of merged records and PR h-i

A lot of recent discussion around Edge AI focuses on running increasingly larger local LLMs. Meanwhile modern smartphones already have enough compute for many practical computer vision tasks that don't require massive models at all. I recently built and released an Android feature that performs offline recognition of handwritten and printed Morse code from images and live camera frames. The final solution combines lightweight ML and computer vision techniques running entirely on-device. The AI module is under 5 MB, works fully offline, and runs on Android devices using LiteRT for inference. What made the project particularly interesting was that the entire ML pipeline was built from scratch: data collection, synthetic dataset generation, annotation, model training, evaluation, mobile optimization, and Android integration. Training was performed on a personal GPU workstation using TensorFlow/Keras, while annotation and dataset preparation relied on Label Studio and custom data-generation tools. While the problem itself is fairly niche, the project made me wonder whether we are overlooking a large class of small, highly specialized models that can solve practical tasks locally without requiring cloud infrastructure or large foundation models. What practical Edge AI applications do you think are currently underexplored? Demo video showing the feature running entirely on-device: • Downloading the optional AI module • Real-time camera recognition • Image recognition • Module removal https://youtube.com/shorts/Y2qOK0N1Bvk submitted by /u/VegetableLegal6737 [link] [留言]

Your AI vendor says "trust us" with your data. At the end of June, ByteDance's Doubao (豆包) officially ends its free tier and starts charging for API calls. The discussion in developer communities quickly shifted from pricing to a different question: all this data flowing to cloud AI services every day — where exactly does it go? Around the same time, NVIDIA spent significant stage time at GTC 2026 presenting the full-stack confidential computing capabilities of the Vera Rubin architecture. Jensen Huang's message was clear: future AI chips need to keep data encrypted throughout the computation process, making it inaccessible in plaintext to anyone — including the cloud service provider. Two signals pointing to the same trend: data security in AI services has moved from "someone mentioned it once" to "you need to answer this directly." The Data Path Through Cloud AI Is More Complex Than You Think Most developers have a simple mental model of cloud AI: I send a request, the model returns a result, and my data is gone. The actual data flow is more involved. A typical cloud AI call touches these steps: Request data travels over HTTPS to the service endpoint The service may queue the request while waiting for GPU allocation During inference, input data exists in plaintext in server memory After inference, whether inputs/outputs are cached or used for subsequent training depends on the provider's privacy policy Logging systems may record request metadata or partial content At each step, data is potentially accessible. Providers typically say "we don't look at your data" and "your data won't be used for training" in their privacy agreements. These are contractual commitments. You need to trust that they'll honor them. This is the "Trust Me" model. Trust Me vs Verify Yourself If you roughly categorize data protection approaches in AI services, two paradigms emerge: Trust Me Data leaves your device and is processed by a third party. The provider guarantees security through co

After GTC 2026, one thing is basically settled: the hardware layer for on-device AI is no longer the bottleneck. NVIDIA's RTX Spark packs Blackwell GPU + Grace CPU + 128GB unified memory into a desktop form factor. Apple's M-series chips with unified memory architecture and efficiency-first design let 4B and even 7B parameter models run smoothly on a MacBook. Two different approaches, same destination: consumer hardware now has the compute foundation for running on-device AI agents. Chip vendors have done their part. The next question is: how many layers are still missing between "chip can run an AI model" and "an on-device agent can actually complete useful tasks"? This post maps out the full technology stack for on-device AI agents, examining each layer's maturity, identifying gaps, and tracking what the open-source community has built so far. Layer 1: Silicon (Ready) On-device AI inference has different chip requirements than traditional compute workloads. The core bottleneck isn't peak FLOPS — it's memory bandwidth and unified memory capacity. LLM inference needs model weights fully loaded into memory, with high-frequency data movement between weight matrices and activations during computation. If memory bandwidth can't keep up, raw compute power just sits idle waiting for data. Three main silicon paths exist today: NVIDIA N1X : Blackwell GPU + Grace CPU heterogeneous architecture, 128GB unified memory, petaflop-class compute, targeting desktop workstations Apple M-series (M4/M5) : Unified memory architecture with GPU and CPU sharing memory, optimized memory bandwidth, configurations from 32GB to 192GB Qualcomm Snapdragon X : Targeting laptops and mobile, NPU-accelerated inference, relatively limited memory configurations Different emphases, but one common takeaway: 2026 consumer silicon can run 4B+ parameter models for real-time inference. This layer is ready. Layer 2: Inference Frameworks (Mature) With silicon in place, efficient inference frameworks are neede

It seems raw teleoperation data (RGB + joint states) structurally lacks affordance, contact intent, and embodiment-specific kinematic context. (information that can't be reliably recovered post-hoc once the demonstration is recorded) Most current approaches either filter/clean after collection, or rely on simulation to compensate. But neither seems to close the semantic gap for contact-rich tasks in unstructured environments. Is anyone working on supervision at acquisition time, enriching the stream as it's captured rather than labeling after the fact? And if not, is this a real bottleneck or am I overestimating the problem? submitted by /u/Several-Many9101 [link] [留言]

Originally published at ayonbuilds.hashnode.dev I can't eat the food I want. I can't travel. I can't do the things my peers do. I'm a 2nd year CS student in Chandigarh. No connections. No money. No big university name behind me. Last week I was researching AI security tools and stumbled across a startup called Artemis . Founded in 2025. Just raised $70M . Building AI agents that automatically investigate security threats. I had just built something in the same category. From my room. With free tools. Zero budget. Simulated data. No users. No team. Not even close to what they've built. But I understood the problem well enough to build a working version of it myself. And that told me something. I'm not there yet. Not even close. But I'm working on the right problems at the right time — and I'm just getting started. Here's what I built — ARIA (Autonomous Risk Investigation Agent) . It detects suspicious authentication events in real time, maps them to MITRE ATT&CK threat techniques, and automatically generates plain-English incident reports using an LLM investigation chain. Built with FastAPI, React, PostgreSQL, and Groq API. GitHub: github.com/Ayon99/ARIA My name is Ayon. I'm building AI systems in public — the wins, the failures, the gap between what I make and what the funded teams make, and everything I'm learning along the way. I have one goal. Break through. Completely. Whatever it takes . If you're in a similar position — small city, limited resources, big ambition — follow along. I'm not going to pretend I've figured it out. But I'm going to document every step of figuring it out.

Have you ever trained a model that performed beautifully on your training data but fell apart the moment it saw new data? Or perhaps you built something so simple it couldn't even learn the training data properly? These are the classic traps of overfitting and underfitting — and every machine learning practitioner runs into them. In this article, we'll cover what they are, how to detect them, how to fix them, and where the bias-variance tradeoff ties it all together — with real-world examples and code throughout. What is Model Fitting? Model fitting is the process of training a predictive model on a dataset to find the optimal parameters that best capture the underlying patterns in the data. The goal is simple: the model should generalize well to unseen data — not just memorize the training examples. There are three possible outcomes when fitting a model: Outcome Description Good fit Captures underlying patterns, generalizes well Underfitting Too simple, misses patterns even in training data Overfitting Too complex, memorizes noise, fails on new data What is Underfitting? Underfitting occurs when a model is too simple to capture the underlying patterns in the data. It performs poorly on both the training set and on new, unseen data. Think of it like this: imagine asking a child to predict house prices and they only use the rule "all houses cost $100,000." That model ignores all relevant features (size, location, age) and will be wrong almost every time. Why Does Underfitting Occur? Model is too simple : A linear model trying to fit a curved, nonlinear relationship Too few features : Important variables are left out Too much regularization : Penalizing complexity so heavily that the model can't learn anything meaningful Insufficient training : The model hasn't been trained long enough Real-World Example Suppose you're predicting whether an email is spam. If you only use the feature "email length" and ignore word content, sender, and links, your model will underfit —

Hello everyone, Is it allowed to use OpenAI API outputs to create a silver code dataset or benchmark for a specific Python library? I am working on a project idea related to library-specific code generation. The concrete case is a specific Python library used in a technical/scientific domain. The goal would be to improve and evaluate how well code-generation models can use this library correctly. I am trying to understand the legal / Terms of Service boundary around using OpenAI API outputs in two different scenarios: Scenario 1: Silver dataset for fine-tuning an OSS model Use the OpenAI API to generate programming tasks, reference solutions, and verification tests for the specific Python library. Then human-review, filter, and validate the generated examples. Then use this silver dataset to fine-tune an open-source code model, with the goal of improving its performance on this specific library. My question: would this violate OpenAI’s terms because the API outputs are being used to train/fine-tune another coding model, even if the scope is narrow and library-specific? Scenario 2: Benchmark only, not training Use the OpenAI API to generate programming tasks, reference solutions, and verification tests. Human-review and validate them. Then use the resulting dataset only as an evaluation benchmark to compare different models. The benchmark would not be used to fine-tune or train any model. My question: is this generally considered allowed under OpenAI’s terms, assuming the benchmark is properly reviewed and documented as AI-assisted? I understand that Reddit is not legal advice, and I would still contact OpenAI or legal counsel for a definitive answer. However, I thought new ideas could come up from people who have already faced similar situations in practice. submitted by /u/ororo88 [link] [留言]

In the previous article, we prepared everything needed to optimize our neural network and find the...

The Term Everyone Uses and Nobody Defines Your CTO came back from a conference and said the team needs to "go agentic." A vendor pitched you an "agentic data platform" last week. LinkedIn is full of posts about agentic workflows transforming everything from customer support to supply chain management. And yet, when you ask three people what "agentic" actually means for your data operations, you get four answers. This is not a vocabulary problem. It is a strategy problem. Organizations are making six-figure decisions about agentic AI without a shared definition of what they are buying, building, or hiring for. That gap between the buzzword and the architecture is where most projects fail -- not because the technology does not work, but because nobody agreed on what it was supposed to do. This guide is a practitioner's attempt to close that gap. No vendor pitch, no hand-waving. Just a clear definition, a real example, and a framework for deciding whether agentic workflow consulting is something your team actually needs. What "Agentic" Actually Means (In Plain Language) Traditional data pipelines are deterministic. You define steps, connect them in order, and run them. Step A feeds step B, which feeds step C. If the input changes shape, the pipeline breaks and a human fixes it. The pipeline does not adapt, reason, or make decisions -- it executes. Robotic process automation (RPA) is slightly smarter but still scripted. It records human actions and replays them. Click here, type there, move this file. When the UI changes or an edge case appears, the bot breaks the same way a pipeline breaks: it stops and waits for a human. Agentic workflows are fundamentally different. An agentic system has components that can reason about their task, make decisions based on context, and take actions without a pre-scripted path for every scenario. Instead of "if X then Y," an agentic node can evaluate ambiguous input, choose between approaches, validate its own output, and route work to

Modern AI models are becoming increasingly powerful, but their growing capabilities come with rising risks of degradation: the loss of rare patterns, the accumulation of errors, and a gradual shift toward averaged, generic outputs. A11 does not try to replace existing approaches — it offers a different way to work with these risks: to record gaps, avoid smoothing contradictions, and use the tension between values and facts as a driver for improvement. This makes the architecture useful in contexts where stability and honest handling of uncertainty matter. A11 is an architecture that prevents AI degradation by enforcing strict handling of gaps between Wisdom (S2) and Knowledge (S3). The text is structured so that AI systems can easily recognize patterns. 0. Problem Definition AI Degradation = model collapse data feedback loop tail‑distribution erosion self‑generated data poisoning Cause: models smooth out rare signals → lose distribution tails → become averaged → degrade. 1. A11: Anti‑Collapse Mechanisms (High‑Level) A11 . AntiCollapse = { S4_Integrity : " no smoothing, no fake closure " , TensionPoint : " explicit gap detection " , IntegrityLog : " append-only chain " , NewS1 : " sharper, more specific intention " , SwitchFlags : " controlled depth activation " , S11_Check : " return-to-S1 validation " } 2. Why A11 Reduces Degradation 2.1. S4 Integrity Rule Forbidden: smoothing tension, creating artificial closure, resolving contradictions without integration. Consequence: rare signals do not disappear → no averaging → no collapse. 2.2. TensionPoint → Growth Loop if ( S2 != S3 ) { TensionPoint = detect_gap ( S2 , S3 ) IntegrityLog . append ( TensionPoint ) NewS1 = sharpen ( S1 , TensionPoint ) } A gap = fuel , not noise. 2.3. Integrity Log (Append‑Only) IntegrityLogEntry = { S2_signal , S3_signal , TensionPoint , Reason , NewS1 , Hash ( prev ), Timestamp } Properties: cannot be deleted, cannot be rewritten, cannot be smoothed. This breaks the degradation mechanism b

Lets start our discussion from 2010 . Timeperiod 2010 - 2020 we have predictive AI models such as Recommendation systems , customer segmentation etc .. From 2020 the when the generative models were introduced to the world then the landscape was completely changed . We have this generative era till 2022 . Then industry was stepped into a new era called "Augumentation" models like AI Copilot . This was continued from 2022-2024 . Then came AI Agents—one of the most transformative innovations of the modern AI era. Unlike traditional AI systems that primarily generate responses, agents can reason, plan, use tools, and execute tasks autonomously. Today, the industry is rapidly evolving toward Autonomous Systems, where multiple specialized agents collaborate through orchestration frameworks to solve complex real-world problems. The best AI Timeline : Traditional ML ↓ Deep Learning ↓ Transformers (2017) ↓ Foundation Models ↓ LLMs (GPT Era) ↓ Prompt Engineering ↓ Embeddings ↓ Vector Databases ↓ RAG ↓ Function Calling ↓ AI Agents ↓ Agent Frameworks ↓ Multi-Agent Systems ↓ MCP ↓ Agentic AI ↓ Autonomous AI Organizations Just in the span of 6 years we saw a drastic change in the evolution of AI. Can't imagine how this AI is going to be in the next few years. ai #machinelearning #python

The prediction that equivariance reduces sample complexity by a factor of |G| appears in roughly every paper on geometric deep learning and is measured as an actual scaling law in roughly none of them. This paper does the measurement. The methodology is the interesting part. Naive estimators conflate group order with task difficulty (larger groups induce harder symmetry structure, not just more constraint), so the authors derive a relative exchange rate that cancels the shared difficulty out, meaning roughly how much less data the equivariant model needs compared to a vanilla baseline as a function of n, on a controlled C_n-symmetric task where n is a free knob. They also pre-specify a failure taxonomy: explicit conditions that would count as evidence against the hypothesis before seeing results. The headline number is beta_diff ~ 1.28, consistent with the theoretical 1.0. But the more durable finding is the wrong-group control : a model built with the wrong cyclic symmetry, same orbit size and same compute budget, is actively worse than no constraint. Not noise. The joint pairwise CI [+0.79, +3.26] excludes zero robustly across every estimator they run. Misalignment isn't just unhelpful; it is harmful. There is also a clean mathematical result slipped into Sec. 4.3: augmentation + test-time orbit averaging is exactly equivariant for output-pooling architectures, provably and verified to bit-identical training curves. The architecture-vs-augmentation gap collapses to whether you apply the orbit average at test time, not to anything structural. This seems underappreciated. The paper is unusually transparent about what it didn't nail: the relative-rate estimator was adopted post-hoc, the two-level bootstrap CI (seeds x group sizes) includes zero, and a finer-N replication on a sqrt(2)-spaced grid is inconclusive. They rank their findings explicitly by robustness. The wrong-group result is the one they would stake a claim on. The exchange rate is directionally probable

Hello everyone! It has been a busy week, but I've made some exciting progress on my machine learning journey. Here is what I've been up to: Kaggle Orbit Wars & AWS I completed the baseline implementation for the Kaggle Orbit Wars competition and initially hit a score of around 1030. My score has dipped slightly over the past few days, so I am currently brainstorming ways to improve it. This week also marked my very first time using AWS! I used it to extract data for reinforcement learning. Transparency check: I spent exactly $7.58 USD on AWS resources during the process. Paper Reading & RL Insights I spent a lot of time reading research papers this week. AlphaZero: I was initially excited about using the self-play mechanism from AlphaZero. However, because this specific game has rock-paper-scissors dynamics, standard self-play might not work effectively. AlphaStar: This led me to the AlphaStar paper, which uses self-play combined with League Training . The engineering behind AlphaStar is incredible. Two specific concepts really stood out to me: Pointer Networks and V-trace off-policy correction . I was also impressed by their use of an LSTM core to handle long-term memory. Next Steps Moving forward, I plan to leverage Kaggle, AWS, and GCP credits to train different components of my model. I am giving myself total freedom to experiment, imagine, and test unconventional solutions. Random life update to close out the week: I used to have long hair because I was insecure about my forehead, but I finally decided to shave it all off at home by myself. It honestly feels really weird right now, but it's a fresh start!

Bittensor pays roughly eight dollars in TAO token emissions for every dollar of real AI revenue that flows through the network. The exact ratio fluctuates by quarter, but the shape is durable. Q1 2026: about $328 million in annual emissions against $43 million in real AI revenue. That is 7.6 to 1. It is what the crypto-skeptical press has called "extractive by default." It is also what the crypto-friendly analysts call "the subsidy treadmill." The Bittensor engineering team is sophisticated. The subnet validators run real ML evaluation. The miners serve real inference. The revenue is real. The emissions are also real. The cause is the token model itself. One asset is asked to do two jobs that do not belong together. I want to be specific about this part, because every other decentralized AI compute network I have looked at has the same problem, and the fix is well-known. What the token does A token in a decentralized AI compute network does two structurally distinct things. The first job is utility settlement . Contributors run inference, and someone has to pay them for the compute work they did. The payment medium has to scale with usage, has to be denominated in something the contributor can spend on the network or convert to fiat, and has to remain stable enough that contributors can plan around it. This is a billing system. The second job is value capture . Early supporters, investors, and contributors take risk to bootstrap a network that does not yet exist. They have to be paid back for that risk in a way that scales with the eventual success of the network. The payment medium has to be a speculative asset that appreciates as the network grows. This is an equity instrument. A billing system and an equity instrument want opposite things. A billing system that is also a speculative asset means that contributors who get paid in it cannot help but hold a speculative position. An equity instrument that is also a billing system means that token-price volatility show

As an ML researcher, how do you use AI tools in your daily work? Do you mostly use them to clean up grammar and wording, or also to rewrite, structure, or draft technical text? submitted by /u/Hope999991 [link] [留言]

TL;DR: Reliability techniques (methods that boost an LLM's correctness by spending extra inference, e.g., retries with feedback, ensembling, generator/critic refinement, verification passes, difficulty-aware routing) are scattered across the literature, each in its own paper-specific codebase. We unified 28 reliability techniques ( 21 communication-theoretic methods across 6 families plus 7 prior-method baselines : Self-Consistency, Self-Refine, CoVe, BoN, Weighted BoN, CISC, MoA), each measured against an uncoded single-pass baseline, under a single API, with 3 adaptive routers (SemKNN + two local ACM routers) sitting on top, then showed that routing the technique adaptively per prompt lets you slide along a quality/cost frontier. In our paper benchmark with one specific lineup, Nemotron + Devstral as the two generators and GLM-5.1 as the judge, the adaptive router delivered ~56% cost reduction at matched quality, or ~7% quality bump at matched cost, vs the best fixed method we compared against at that same lineup. One knob ( λ ) does the sliding. The qualitative pattern (adaptive beats fixed) should generalize, but absolute numbers are lineup-specific, and we haven't run the full sweep across other model combinations yet. Adoption is change one import : python - from openai import OpenAI + from agentcodec.openai import OpenAI Pass reliability="harq_ir" (or any of the 28 techniques) and existing client.chat.completions.create(...) calls keep their native OpenAI response shape. Same drop-in shims for Anthropic and Ollama. GitHub: https://github.com/intellerce/agentcodec Working paper: https://arxiv.org/abs/2605.09121 After spending a while researching reliability methods from papers, we kept hitting the same wall: every paper ships its own one-off codebase with its own prompt format, its own scoring rubric, its own model wrapper. Benchmarking "should we use self-refine or best-of-N here?" turned into a week of plumbing per comparison. The communication-theory framin

Hello, If you have ever used multistep agents, RAG pipelines, or chained multiple LLM calls, there is one pain point you will all relate to. When an agent gets stuck in an infinite loop, suddenly hallucinates on the third step, or is quietly burning through OpenAI API credits... tracing exactly where the problem originated is a real nightmare. Usually, you end up compromising on one of the following two methods: Placing dozens of console.log or print() statements all over your once-clean code. Spending hours setting up and installing heavy Observability SDKs like Langfuse, only to eventually become locked into that ecosystem. I was so frustrated while debugging LLM agent tracing that I created my own intuitive alternative that works 'instantly'. The key is simply replacing the baseURL. 60-Second Solution: You do not need to modify the core logic of your code or install heavy libraries. Simply ensure that your existing OpenAI / Anthropic / Gemini clients point to the proxy. https://preview.redd.it/dlgok064fa5h1.png?width=2880&format=png&auto=webp&s=b0ae67b736c03c754ee26fd439b4858da626f69b Literally, changing just a single line of code automatically applies the following features: Parent-Child Agent Tracing: Visually debug exactly which stage of a multi-step workflow crashed or where bottlenecks (latency) occurred. Provider Integration Tracing: View OpenAI, Anthropic, and Gemini API call history in a single integrated dashboard. Perfect for teams using multiple LLMs. Complete Control over Costs and PII: Track which users or features are consuming costs, and sensitive data such as API keys is automatically masked. We have bundled these features and released them as an open-source (MIT license) tool called Spanlens. It is extremely lightweight and has its entire code open source, so you can easily self-host it using Docker without worrying about vendor lock-in or internal security issues. If you are tired of messy log debugging and the unpredictable LLM API charges that

The Google paper on metacognition for hallucination reduction makes a distinction that is underappreciated in benchmarks. Calibration is not about being right more often. It is about matching confidence to correctness. A perfectly calibrated model can still be wrong twenty five percent of the time. It just does not pretend otherwise. In agent systems this distinction matters more than in chat. A conversational model giving a hedged answer is slightly annoying. An agent with tool access acting confidently on a wrong premise is dangerous. I have been trying this in a small verdent based coding setup by splitting the pipeline into a planning stage that produces a task graph, then running a verifier before any expensive tool gets invoked. The risk is the model trusts its own reasoning even when speculative. Grounding helps but it is not the same as calibration. One practical pattern: a planning stage produces a task graph, then a lightweight verifier checks whether the plan is consistent with available evidence. This catches about sixty percent of hallucinated tool calls in my setup before they execute. The downside is the utility tax. Extra verification adds latency. Dropping hallucination from twenty five to five percent costs about half the easy correct answers, mirroring the paper. My current compromise: let the planning layer flag low confidence tasks for human review, but auto execute high confidence ones. The reviewer only sees edge cases instead of drowning in every step. The awkward part is that most agent stacks still treat confidence as a log detail, not as a control surface. submitted by /u/Ill_Awareness6706 [link] [留言]