Hugging Face Trending Papers
INTACT: Inducing Latent Reasoning on a Frozen Backbone
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arXiv Machine Learning
4d ago
INTACT: Inducing Latent Reasoning on a Frozen Backbone
INTACT introduces a small external encoder that induces latent reasoning in a frozen backbone model by mapping hidden states to compact continuous representations, called soft-token blocks. The encoder is trained via distillation to mimic written reasoning steps while preserving the backbone’s behavior, and then fine‑tuned for answer correctness. Experiments on four frozen backbones (1B–8B) show that INTACT improves accuracy on GSM8K and other out‑of‑domain benchmarks, reduces latency by up to 19%, and outperforms existing latent‑reasoning baselines at 3B and 8B scales.
arXiv AI
Sep 2
Latent Recurrent Thoughts: Recurrent Refinement of Proposed Latents for Reasoning with Frozen LLMs
Latent Recurrent Thoughts (LRT) proposes a method for reasoning with frozen large language models by operating in the model’s continuous representation space. A small auxiliary network generates initial latent vectors, which a tiny recurrent reasoner refines over multiple steps, decoupling computational depth from model size. Experiments on symbolic and natural‑language reasoning tasks show that LRT outperforms prior frozen‑decoder continuous‑space methods and chain‑of‑thought prompting while using far less inference compute.
arXiv AI
Aug 11
Think Deep, Speak Once: Relit, A Recursive Latent Implicit Transformer Framework
arXiv:2608. 08113v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has become the dominant paradigm for eliciting reasoning in Large Language Models (LLMs), yet it creates substantial computational overhead by forcing models to externalize intermediate reasoning steps as discrete tokens.
arXiv AI
Oct 9
From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers
The paper introduces LLoCoT, a looped latent‑reasoning framework that replaces the traditional left‑to‑right generation of chain‑of‑thought tokens with iterative refinement of a compact latent workspace using a shared transformer. By jointly updating latent slots over a few iterations and sampling latent tokens in parallel, LLoCoT feeds these into an autoregressive decoder for answer generation. Experiments on HumanEval and MBPP show that LLoCoT matches or surpasses explicit‑CoT baselines while dramatically reducing reasoning latency and increasing throughput.
arXiv AI
Jun 16
Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate
arXiv:2606. 16360v1 Announce Type: cross Abstract: Chain-of-thought (CoT) prompting improves reasoning in large language models (LLMs) by externalizing intermediate computation as discrete text tokens, but this textual interface also introduces redundancy and inference overhead.
arXiv AI
Jul 29
Penelope: Localized Latent Recurrence for Efficient Structured Reasoning
arXiv:2607. 25915v1 Announce Type: new Abstract: Complex structured reasoning tasks often require additional computation, yet current language models obtain it mainly by increasing parameter scale or by serializing intermediate steps as chain-of-thought (CoT) tokens.