Entity · model
Gemma-4 E4B-it
modelactive
gemma-4-e4b-it-dff312f4·6 events·first seen Aliases: Gemma-4 E4B-it, Gemma-4, Gemma-4-E4B-it, Gemma4-12B
Co-occurring entities
Looped TransformerOpenClawQwen3-4BNanbeige4.2-3BGraded Entity-Familiarity Readouts in Language Models: Polish Adaptation, Cross-Language Robustness, and Refusal SteeringBielikGemma 4Qwen3PLLuMClaude Sonnet 4.5GoogleWhere Do Models Find Happiness? Emotion Vectors in Open-Source LLMsApertus-8B-Instruct-2509AnthropicTowards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task LearningLoRAMIMIC-ESIiCRAFTMDMAP-ElitesLlama-3
More like this (12)
Recent events (6)
Nanbeige4.2-3B: A 3B-parameter agentic model outperforming 9B–12B models on agentic benchmarks
Researchers present Nanbeige4.2-3B, a 3-billion non-embedding parameter model designed for agentic tasks including code-agent, office-agent, and complex tool use. The model is pretrained on 28T tokens using a Looped Transformer architecture that reuses layer stacks to increase capacity without adding parameters, and trained with a multi-stage RL pipeline combining mixed-mode RLHF, length-controlled reasoning RL, and agentic RL with outcome and process rewards. Evaluations claim Nanbeige4.2-3B outperforms larger models including Qwen3.5-9B and Gemma4-12B on diverse agentic benchmarks while remaining competitive on reasoning tasks. The result contributes to the ongoing question of how much agentic capability can be packed into compact, locally-deployable models.
Graded entity-familiarity probes in LLMs enable refusal steering and cross-language robustness
Researchers probe activations at the final prompt token across twelve instruction-tuned models (Bielik, PLLuM, Gemma-4, Qwen3 families) to assess whether LLMs encode graded familiarity with named entities before generating answers. Using a new dataset of 1,440 Polish entities across popularity deciles plus fabricated controls, they find familiarity probes reliably separate real from fabricated entities and track popularity in Polish-adapted models. A key finding is that a single one-dimensional familiarity direction injected at one layer in Gemma-4-12B can steer refusal rates monotonically from 0.24 to 1.00 (or 0.73 to 0.00), revealing a separation between representational familiarity and the policy that converts it into abstention behavior.
Emotion vectors replicated in open-weight LLMs with architecture-dependent valence geometry
A new arXiv preprint extends prior findings on emotion vectors in Claude Sonnet 4.5 to two open-weight models, Apertus-8B-Instruct-2509 and Gemma-4-E4B-it, by extracting emotion contrast vectors across all layers. The authors recover valence geometry in both models (peak PC1-valence correlations of r=0.76 and r=0.83, near Claude's r=0.81) but find notable architectural differences: Gemma encodes valence strongly in early layers while Apertus shows the opposite pattern. Arousal encoding proves sensitive to the corpus used for extraction, suggesting uneven distribution of arousal-relevant cues across model-generated text.
Judge-Aware Gated Multi-Task Learning achieves state-of-the-art on UK Employment Tribunal outcome prediction
Researchers propose a Judge-Aware Gated Multi-Task Learning architecture for legal outcome prediction that explicitly disentangles factual case merits from judicial discretion via a gated fusion mechanism conditioned on judge identity. Evaluated on 13,937 UK Employment Tribunal decisions, the approach outperforms supervised fine-tuning of a Gemma-4 26B backbone while requiring an order of magnitude fewer trainable parameters. The key finding is that differentiable structured composition of identity signals outperforms prompt-based composition over a much larger generative model, suggesting conditioning interface choice dominates scale for identity-conditioned classification tasks.
LLM-guided MAP-Elites evolution improves medical decision pipelines at inference time
Researchers propose using LLM-guided MAP-Elites evolutionary search as an inference-time alternative to fine-tuning for adapting LLMs to clinical workflows, formulating triage, consultation, and image classification as evolutionary searches over executable artifacts. Across three medical settings, evolved programs substantially outperform manually designed baselines: triage accuracy improves from 77.3% to 87.1% and emergency recall from 0.60 to 0.97, with gains also shown on MIMIC-ESI, iCRAFTMD, and PneumoniaMNIST. The approach works across Llama-3, Qwen-3.5, and Gemma-4 backbones and produces interpretable program-level mechanisms rather than superficial prompt changes.
AlignAtt4LLM adapts simultaneous speech translation policy to decoder-only LLMs for IWSLT 2026
Researchers present AlignAtt4LLM, a simultaneous speech translation system for IWSLT 2026 covering English to German, Italian, and Chinese. The system cascades Qwen3-ASR for incremental transcription with Gemma-4 E4B-it for translation, applying a novel AlignAtt policy adapted for decoder-only LLMs that lack encoder-decoder cross-attention. Key contributions include explicit source span prompting, offline alignment head selection, and query/key capture to recover a usable attention-based read/write policy. The system outperforms IWSLT 2026 baselines for European language pairs in both low- and high-latency regimes.