arXiv:2606. 29824v1 Announce Type: cross Abstract: While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging.
While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires continuous environmental interaction, yet current agents lack the necessary persistent procedural memory.
arXiv:2604. 02029v2 Announce Type: replace Abstract: Latent space is rapidly emerging as a native substrate for language-based models.
arXiv:2601. 03555v3 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.
arXiv:2604. 27660v3 Announce Type: replace Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge.
arXiv:2605. 09159v2 Announce Type: replace Abstract: Recent work shows that large language models (LLMs) encode behavioral traits ("personas") as linear directions in activation space, often called "persona vectors".
The paper introduces AlgoTrace, a framework that traces and steers algorithmic operations in large language models’ latent space during multi‑step reasoning. By clustering latent activations on tasks such as TSP, 3SAT, AIME, and Graph Navigation, the authors identify reusable primitive vectors that can be injected to elicit specific algorithmic behaviors, composed algebraically, and transferred across models and tasks. Fine‑tuning further improves the composition of these primitives, suggesting that LLM reasoning can be viewed as a walk through algorithmic primitives governed by compositional geometry.
A*-Thought-V2 is a framework that models Chain-of-Thought reasoning as a geometric trajectory in a 3D PCA space, using explicit-implicit latent tokens to compress steps that deviate from the main question-to-solution direction. The method measures alignment angles to decide which steps remain text and which become latent, and introduces stepwise embedding forcing and label forcing to train the architecture. Experiments on Qwen models show up to 2.6% accuracy gains, halved response length, and significant reductions in computation and training time.
arXiv:2505.16782v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT...
Instruction tuning is often thought to give language models a universal ability to follow instructions, but this study shows otherwise. By probing nine tasks across three models, the authors find that general probes reveal selective, not uniform, deficits, cross‑task transfer is weak and skill‑similar, and causal ablation uncovers sparse, asymmetric dependencies. The results suggest instruction following is a coordinated use of diverse linguistic skills rather than a single shared mechanism.
arXiv:2608. 16806v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, driving their gradual evolution from text generation models into the core of agents capable of perceiving environments, invoking tools, and executing tasks.
The paper investigates the effectiveness of activation steering in latent chain-of-thought (CoT) reasoning compared to explicit CoT. It finds that steering continuous latent thoughts yields weaker impacts on language generation, even when hidden representations are shifted similarly. The authors propose a latent-to-language transition gap, supported by evidence of abrupt output distribution changes at the transition boundary and weaker bidirectional control in latent CoT.