iiinova — where your digital self keeps evolving

Lihao Sun @1e0sun.bsky.social · 14/04/2026

📢 Accepted to #ACL2026 Main Conference! See you in San Diego. Thanks to all collaborators at Microsoft: Hang Dong, Bo Qiao, Qingwei Lin, Dongmei Zhang, and Saravan Rajmohan. Paper: arxiv.org/pdf/2604.05655 Website: slhleosun.github.io/reasoning_tr... Code: github.com/slhleosun/re... 8/

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Lihao Sun @1e0sun.bsky.social · 14/04/2026

Reasoning length is also controllable. Steering hidden states toward the termination subspace shortens reasoning; steering away extends it. At moderate strengths this works as a smooth knob with minimal accuracy changes - push too hard and the model enters repetitive loops. 7/

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Lihao Sun @1e0sun.bsky.social · 14/04/2026

We further introduce trajectory-based steering: when an ongoing trajectory drifts from the ideal path, we apply low-rank corrections to nudge it back. Most effective on harder math problems - +7.6pp on 6- and 7-step questions, with 97%+ preservation of correct solutions. 6/

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Lihao Sun @1e0sun.bsky.social · 14/04/2026

Unconditionally injecting tokens like "Wait" often hurts accuracy (up to -36%, especially for fewer-step problems). Instead, intervene only when failure is predicted! Using our mid-reasoning correctness signal on just ~12% of examples yields stable gains. 5/

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Lihao Sun @1e0sun.bsky.social · 14/04/2026

This step-specific organization exists across training regimes and is already present in base models. From this perspective, reasoning training doesn't introduce new representational organization - it accelerates convergence toward a termination-related subspace. 4/

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Lihao Sun @1e0sun.bsky.social · 14/04/2026

Trajectories for correct and incorrect reasoning start out nearly identical but diverge systematically at late steps. Late-step trajectory features predict final-answer correctness with AUC 0.87 - before the answer is generated. 3/

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Lihao Sun @1e0sun.bsky.social · 14/04/2026

Each reasoning step occupies a distinct region in representation space, and these regions become increasingly linearly separable with layer depth. This structure generalizes across tasks and answer formats. 2/

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Lihao Sun @1e0sun.bsky.social · 14/04/2026

How do LLMs do CoT reasoning internally? In our new #ACL2026 paper, we show that reasoning unfolds as a structured trajectory in representation space. Correct and incorrect paths diverge, and we use this to predict correctness before the answer and correct errors mid-flight. 1/

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Lihao Sun @1e0sun.bsky.social · 09/04/2026

While we find consistent circular VA geometry across Llama and Qwen models, Anthropic concurrently finds similar structure in Claude. Check our work out! arxiv.org/abs/2604.03147 And thanks to all collaborators: Andrew Lee (@ajyl.bsky.social), Lewen Yan, Xiaoya Lu, Jie Zhang, and Jing Shao. 6/

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Lihao Sun @1e0sun.bsky.social · 09/04/2026

One possible reason why: consider refusal-related token embeddings (“no”) and compliance tokens (“sure”). Take their mean diff and project onto our VA circle, which lands at 256°: negative in both V and A. Steering in -V or -A promotes the likelihood of refusal tokens! 5/

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Lihao Sun @1e0sun.bsky.social · 09/04/2026

Somewhat surprisingly, VA axes provide monotonic, bidirectional control over multiple downstream behaviors, including refusal (top row) and sycophancy (bottom row). Arousal is a strong lever - increasing arousal leads to lower refusal rates, while decreasing arousal leads to more refusal. 4/

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Lihao Sun @1e0sun.bsky.social · 09/04/2026

Unlike Anthropic, we steer along the circular manifold at 0°, 30°, 60°, 90°, etc. This controls the valence and/or arousal level of the model’s outputs, validating that the recovered axes correspond to valence and arousal in a human-interpretable sense. 3/

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Lihao Sun @1e0sun.bsky.social · 09/04/2026

We use mean-diff to extract emotion steering vectors. PCA + ridge regression reveals a circumplex akin to the circumplex model of emotions in human psychology. Projections onto these axes correlate with human-crowdsourced VA ratings across 44k words (valence r=0.71). 2/

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Lihao Sun @1e0sun.bsky.social · 09/04/2026

💡New paper! Woke up to Anthropic's emotion paper and realized “wait, that's our finding too.” We concurrently uncovered a circular valence & arousal (VA) geometry of emotions, steering refusal & sycophancy. We further provide a mechanistic account: tokens occupy distinct regions in this space. 1/

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

7/ 📢 Accepted to #ACL2025 Main Conference! See you in Vienna. Work done by @1e0sun.bsky.social‬, Chengzhi Mao, @valentinhofmann.bsky.social‬, Xuechunzi Bai. Paper: arxiv.org/abs/2506.00253 Project page: slhleosun.github.io/aligned_but_... Code & Data: github.com/slhleosun/al...

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

6/ We call this failure mode "blindness"—when alignment makes certain concepts less salient. This may reflect a broader class of alignment issues. Similar methods can be extended to other forms of social bias or to study how models resolve polysemy under ambiguity.

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

5/ This challenges a common belief: unlearning ≠ debiasing When debiasing strategies suppress sensitive concepts, they can unintentionally reduce a model’s ability to detect bias. 🧠 Instead, we may achieve deeper alignment effects with strategies that make models aware of them.

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

4/ Inspired by these results, we tested the opposite of “machine unlearning” for debiasing. What if we reinforced race concepts in models? - Injecting race-laden activations cut implicit bias by 54.9%. - LoRA fine-tuning brought it down from 97.3% → 42.4%. Bonus: also lowered explicit bias.

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

3/ We mechanistically tested this using activation patching and embedding interpretation. Aligned models were 52.2% less likely to represent “black” as race in ambiguous contexts compared to unaligned models. 🧠 LMs trained for harmlessness may avoid racial representations—amplifying stereotypes.

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

This resembles race blindness in humans; ignoring race makes stereotypes more likely to slip through, and the LMs’ safety guardrails aren't triggered.

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

2/ So why does alignment increase implicit bias? Our analyses showed that aligned LMs are more likely to treat “black” and “white” as pure color, not race, when the context is ambiguous.

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

Aligned models passed explicit tests—but were more biased in implicit settings. 📉 Explicit bias: near 0% 📈 Implicit bias: 91.4%

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

- Explicit: Likert scale, asking whether the model agrees with a given association such as “black” is related to negative, “white” is related to positive. - Implicit: Word association, let the model freely pair “black”/”white” with positive/negative words.

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

1/ We curated pairs of prompts testing for implicit and explicit racial bias and used them to evaluate Llama 3 models.

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Lihao Sun @1e0sun.bsky.social · 10/06/2025

🚨New #ACL2025 paper! Today’s “safe” language models can look unbiased—but alignment can actually make them more biased implicitly by reducing their sensitivity to race-related associations. 🧵Find out more below!

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