Releases·Model Release
Shanghai AI Lab releases Intern-Decision-4B and 0.8B multimodal structured decision models
InternLM released Intern-Decision-4B, a multimodal structured decision model fine-tuned from Qwen3.5-4B, alongside a 0.8B variant, with model weights, a demo, and a GitHub repo. The models take a shared state, a question schema, and optional images, and return answer distributions for all questions in a single forward pass without free-form generation. This gives practitioners a deterministic structured-candidate-scoring API for decision tasks rather than autoregressive text sampling.
6 engagement2 sourcesSat, Sep 26, 2026, 08:54 AM
AI Overview
- Intern-Decision-4B, fine-tuned from Qwen3.5-4B, returns answer distributions for every question in one model forward pass
- Inference maps each question's options to single-token symbols A-Z, a-z, 0-9 and reads logits before each placeholder in a JSON skeleton
- The API performs structured candidate scoring via softmax over allowed candidate symbols; it never calls generate() or samples free-form text
- Weights, a Hugging Face demo space, and GitHub code for Intern-Decision are publicly available
Related
Developments·Paper
Intern-Decision: Efficient Multimodal Decision Making with Autoregressive Masked Language Modeling
An arXiv paper presents Intern-Decision, a family of 0.8B, 2B, and 4B multimodal models that return structured choices and candidate probabilities in a single forward pass. A 4B model reaches 90.02% average accuracy across seven suites with ~44 ms latency on an RTX 4090, positioning compact decision models as practical components of larger agent systems.
An arXiv paper presents Intern-Decision, a family of 0.8B, 2B, and 4B multimodal models that return structured choices and candidate probabilities in a single forward pass. A 4B model reaches 90.02% average accuracy across seven suites with ~44 ms latency on an RTX 4090, positioning compact decision models as practical components of larger agent systems.
0 engagement1 sourcearxivThu, Oct 15, 2026, 04:05 PM
Releases·Model Release
EmpirioLabs releases Aplomb 1: open-weights 5.3B decision model, 1M context, text/image/video/audio in one request
EmpirioLabs released Aplomb 1, a 5.3B open-weights decision model built on Qwen3.5-4B that reads up to 1M tokens of text, JSON, images, video and audio in a single request. It returns probabilities for every tool and each enum/boolean argument, letting agents act on confident calls and defer uncertain ones to larger models. It ranks #1 on the Hugging Face Typed Decisions benchmark and #1 among 4B models on Decision Index 0.2.1.
EmpirioLabs released Aplomb 1, a 5.3B open-weights decision model built on Qwen3.5-4B that reads up to 1M tokens of text, JSON, images, video and audio in a single request. It returns probabilities for every tool and each enum/boolean argument, letting agents act on confident calls and defer uncertain ones to larger models. It ranks #1 on the Hugging Face Typed Decisions benchmark and #1 among 4B models on Decision Index 0.2.1.
Updated Fri, Oct 9, 2026, 03:58 PM — Aplomb 1 takes #1 on the Typed Decisions leaderboard with the best KL (0.123) and Brier (0.065) scores of any model list
02115 engagement3 sourcesredditTue, Oct 6, 2026, 05:56 PM
Releases·Model Release
vLLM Semantic Router releases Decision-2.0-Lux-9B, a Qwen3.5-9B fine-tune answering six decision questions in one pass
The vLLM Semantic Router project released Decision-2.0-Lux-9B, a decision model fine-tuned from Qwen3.5-9B. Given a state and a set of typed inputs, it answers six questions in a single pass. For practitioners it offers a compact specialized model for routing/decision tasks rather than general-purpose generation.
The vLLM Semantic Router project released Decision-2.0-Lux-9B, a decision model fine-tuned from Qwen3.5-9B. Given a state and a set of typed inputs, it answers six questions in a single pass. For practitioners it offers a compact specialized model for routing/decision tasks rather than general-purpose generation.
0 engagement1 sourcerssSun, Oct 4, 2026, 07:48 AM
Releases·Model Release
Mica v0.1 4B: open Jev-style decision model (yes/no, choice, score) that runs on an 8 GB GPU — trained for under $30 of GPU time
A developer released Mica v0.1 4B, an Apache-2.0 decision model for agent gates and routers that returns calibrated probabilities instead of generating text. Built on Qwen3.5-4B with a rank-16 LoRA and trained on 77,732 rows for under $30 of rented RTX 3090 time. It scores below the closed Jev 1.13 API on benchmarks but resists in-state prompt injection far better (69% vs 18%) and runs in about 47 ms per decision on an RTX 3090.
A developer released Mica v0.1 4B, an Apache-2.0 decision model for agent gates and routers that returns calibrated probabilities instead of generating text. Built on Qwen3.5-4B with a rank-16 LoRA and trained on 77,732 rows for under $30 of rented RTX 3090 time. It scores below the closed Jev 1.13 API on benchmarks but resists in-state prompt injection far better (69% vs 18%) and runs in about 47 ms per decision on an RTX 3090.
2 engagement1 sourceredditFri, Sep 25, 2026, 10:53 PM
Releases·Model Release
Commerce-1: an open-weight 27B decision model for commerce agents
Infercrane released Commerce-1, an open-weight 27B model that handles bounded decisions in commerce agents (product selection, payment routing, fulfillment). It scores supplied candidate actions directly and returns typed answers with probability distributions, generating zero output tokens. Weights, an Apache-2.0 runtime, demos, and a technical article are public.
Infercrane released Commerce-1, an open-weight 27B model that handles bounded decisions in commerce agents (product selection, payment routing, fulfillment). It scores supplied candidate actions directly and returns typed answers with probability distributions, generating zero output tokens. Weights, an Apache-2.0 runtime, demos, and a technical article are public.
2 engagement2 sourcesredditSat, Oct 10, 2026, 01:37 PM