arxiv: 2605.09867 · v1 · submitted 2026-05-11 · 💻 cs.LG · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Continuous Latent Contexts Enable Efficient Online Learning in Transformers

Emile Anand , Abdullah Ateyeh , Xinyuan Cao , Max Dabagia

Authors on Pith no claims yet

Pith reviewed 2026-05-12 05:02 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords transformersonline learninglatent contextsin-context learningQ-learningweighted majority algorithmpersistent statedecision making

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The pith

Transformers implement weighted majority and Q-learning using continuous latent context tokens to store state.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that transformers equipped with a small number of continuous latent context tokens can maintain and update algorithmic state across long sequences to perform online decision-making. These tokens encode the state of procedures like the weighted majority algorithm and Q-learning as linear combinations of feature embeddings, allowing adaptation based on feedback without parameter changes. Explicit constructions prove this is possible in constant-depth models, and a trained small GPT-2-style transformer using multi-curriculum training without direct latent supervision outperforms larger models on synthetic online prediction tasks. A sympathetic reader cares because this offers a compact way for models to handle interactive, feedback-driven settings where static in-context learning falls short.

Core claim

We give explicit constructions of constant-depth transformers that implement two foundational online decision-making procedures -- the weighted majority algorithm and Q-learning -- by storing their algorithmic state as linear combinations of feature embeddings, using a small number of latent context tokens. We further train a small GPT-2-style transformer with latent contexts using a multi-curriculum objective that does not directly supervise the latent states. On long synthetic online prediction sequences, this model outperforms larger and more complex LLMs. Our results suggest that continuous latent contexts provide a simple and effective persistent state for transformers to implement在线学习.

What carries the argument

Continuous latent context tokens that store algorithmic state as linear combinations of feature embeddings across transformer layers.

If this is right

Constant-depth transformers can execute online learning algorithms without any parameter updates.
Algorithmic state persists reliably using only a fixed small number of tokens regardless of sequence length.
Multi-curriculum training allows the model to acquire online behaviors without direct labels on the internal states.
Small models using this method achieve higher accuracy than much larger LLMs on long-horizon online prediction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same latent token mechanism could support other online algorithms such as bandit methods or policy iteration in transformer form.
Persistent contexts might reduce the need for external memory modules in long interactive dialogues or agent tasks.
Scaling the approach to real-world feedback streams like user interactions could produce more adaptive deployed models.
One could test extensions by measuring how well the constructions transfer when the input features come from pretrained embeddings rather than synthetic ones.

Load-bearing premise

The continuous latent context tokens can be reliably maintained and updated across arbitrary-length sequences in a transformer forward pass without degradation or the need for explicit supervision on the latent states themselves.

What would settle it

A trained transformer with latent contexts that fails to match the exact update rules of weighted majority or Q-learning on extended synthetic sequences, or whose performance no longer exceeds larger models without such tokens.

Figures

Figures reproduced from arXiv: 2605.09867 by Abdullah Ateyeh, Emile Anand, Max Dabagia, Xinyuan Cao.

**Figure 2.** Figure 2: Q-learning behavior and internal mechanism. (Left) Learned transformer’s cumulative reward compared to tabular baselines. (Middle) A linear probe can recover the Bellman update signal from the context update, indicating explicit encoding of TD components. (Right) Per-step greedy-action agreement with tabular Q-learning across timesteps and reward distributions. 5.3 Inference on LLMs Frontier LLMs are pretr… view at source ↗

**Figure 4.** Figure 4: Cumulative regret of LLMs on interactive expert prediction against the best fixed expert on the same prefix. expert makes a binary prediction, the LLM makes its own prediction based on these four experts, and then the true outcome is revealed to the LLM. The model is not told to run multiplicative weights or any other online learning algorithm explicitly. Its objective is to be as accurate as possible over… view at source ↗

**Figure 5.** Figure 5: Multiplicative weights vs Transformer Expert weights [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗

**Figure 6.** Figure 6: Tabular Q-learning vs Transformer Q-learning via COCONUT Figures 5 and 6 show side-by-side comparisons between the handwired transformer and the groundtruth algorithms. In both cases, the trajectories are indistinguishable: the MWU construction assigns identical weights to experts over time, and the Q-learning construction produces Q-values that exactly match those of tabular Q-learning. These results pro… view at source ↗

**Figure 7.** Figure 7: (Left) Cumulative regret plot, (Right) Causal attention heatmap on an MWU sequence [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗

**Figure 8.** Figure 8: Evaluations on the transformer with latent context trained on expert data (without super [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗

**Figure 9.** Figure 9: We plot the attention devoted to each expert across all the layers. [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗

**Figure 10.** Figure 10: Full causal attention heatmap on a 4-state, 2-action linear-chain MDP where a0 is optimal. The model concentrates attention on the context slot corresponding to a0, reflecting selection of the maximizing action. In addition, the Update token places strong attention on the visited action at, the reward token rt, and the selected maximizing action a ⋆ , precisely the operands required to form the TD error. … view at source ↗

**Figure 11.** Figure 11: Long-horizon cumulative reward of learned transformer against tabular algorithm baselines. [PITH_FULL_IMAGE:figures/full_fig_p026_11.png] view at source ↗

**Figure 12.** Figure 12: Linear probe of the reward signal from the per-step context update [PITH_FULL_IMAGE:figures/full_fig_p026_12.png] view at source ↗

**Figure 13.** Figure 13: Error in reconstructing the tabular Q-values from the [PITH_FULL_IMAGE:figures/full_fig_p027_13.png] view at source ↗

**Figure 14.** Figure 14: Cumulative reward over a 50-step horizon under six reward distributions, each averaged [PITH_FULL_IMAGE:figures/full_fig_p027_14.png] view at source ↗

**Figure 15.** Figure 15: Cumulative regret of DeepSeek-V3 on three regimes. Regret is measured against the [PITH_FULL_IMAGE:figures/full_fig_p034_15.png] view at source ↗

read the original abstract

Large language models (LLMs) exhibit a strong capacity for in-context learning: Given labeled examples, they can generate good predictions without parameter updates. However, many interactive settings go beyond static prediction to online decision-making, in which effective behavior demands adaptation over long multi-turn horizons in response to feedback, and efficient algorithms in these domains must use compact representations of what they have learned. Recently, continuous transformer architectures with latent chain of thought have shown promise for offline iterative tasks such as directed graph-reachability. Motivated by this, we study whether continuous latent context tokens equip transformers to more effectively realize online learning. We give explicit constructions of constant-depth transformers that implement two foundational online decision-making procedures -- the weighted majority algorithm and $Q$-learning -- by storing their algorithmic state as linear combinations of feature embeddings, using a small number of latent context tokens. We further train a small GPT-2-style transformer with latent contexts using a multi-curriculum objective that does not directly supervise the latent states. On long synthetic online prediction sequences, this model outperforms larger and more complex LLMs, including Qwen-3-14B and DeepSeek-V3. Our results suggest that continuous latent contexts provide a simple and effective persistent state for transformers to implement online learning algorithms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper maps weighted majority and Q-learning onto constant-depth transformers via linear combinations in a few latent tokens, with a small trained model beating larger LLMs on synthetic online tasks.

read the letter

The one thing to know is that this paper gives explicit constructions for constant-depth transformers that implement the weighted majority algorithm and Q-learning by keeping their state in a fixed handful of latent context tokens as linear combinations. They also train a small model with these latents on synthetic online sequences and claim it beats much larger LLMs. The constructions are the novel part. Building on latent chain-of-thought ideas, they show how to reduce the state updates of these algorithms to operations the transformer can do with attention and feed-forwards on the latents, without needing deeper layers or growing memory. The training objective is also sensible because it doesn't supervise the latents directly, which lowers the chance that the model is just memorizing a curriculum. The main weaknesses are around verification and long-sequence behavior. The details of how the weights are set to get exact multiplicative updates or Q-value backups aren't in the abstract, and even if they work for one step, the softmax and layer norms could cause the latents to drift over hundreds of steps, breaking the exact equivalence. The experiments use synthetic data, but without knowing how it's generated or how the big models were prompted, the outperformance is hard to trust fully. If the constructions are only approximate, that changes the story. This paper is for people thinking about persistent state in transformers for interactive or online settings. It would be useful to anyone working on in-context learning beyond static prediction. I think it deserves peer review. The idea is concrete enough that referees could check the constructions and ask for better experiments, and it might lead to something practical if the math works.

Referee Report

2 major / 2 minor

Summary. The paper claims that constant-depth transformers equipped with a small number of continuous latent context tokens can exactly implement two core online learning algorithms—the weighted majority algorithm and Q-learning—by representing algorithmic state as linear combinations of feature embeddings. It further shows that a small GPT-2-style transformer trained with a multi-curriculum objective (without direct latent supervision) outperforms much larger models such as Qwen-3-14B and DeepSeek-V3 on long synthetic online prediction sequences.

Significance. If the constructions hold, the work supplies a concrete mechanistic account of how transformers can maintain persistent algorithmic state for online adaptation, linking classical online learning theory to modern architectures. The explicit constructions are a notable strength, as is the demonstration that latent contexts can be learned without direct supervision. The empirical outperformance on long-horizon tasks, if reproducible, would indicate practical utility for interactive decision-making settings where standard LLMs falter.

major comments (2)

[§3.1–3.2] §3.1–3.2 (WMA and Q-learning constructions): The claim that a fixed-depth transformer exactly reproduces the multiplicative weight updates of WMA and the Bellman updates of Q-learning at every step relies on the attention and FFN layers performing precise linear combinations in the latent tokens while leaving other computations identity-like. No argument is given that the composite map over arbitrary T steps remains faithful once softmax normalization, layer norms, and residual connections are applied; single-step mechanics do not automatically guarantee multi-step stability when inputs vary.
[§4] §4 (empirical evaluation): The statement that the trained latent-context model outperforms Qwen-3-14B and DeepSeek-V3 on long synthetic sequences is load-bearing for the practical claim, yet the manuscript provides insufficient detail on sequence generation, feature embedding construction, baseline prompting, number of independent runs, and statistical testing. Without these, it is impossible to determine whether the reported gains are robust or sensitive to the particular synthetic distribution.

minor comments (2)

[Abstract] The abstract references prior latent chain-of-thought work but does not include a citation; adding the relevant reference would improve context.
[§2] A small diagram illustrating the flow of latent context tokens across time steps would clarify how the state is updated and read out.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and have revised the manuscript to strengthen the presentation of the theoretical constructions and to supply the requested empirical details.

read point-by-point responses

Referee: [§3.1–3.2] §3.1–3.2 (WMA and Q-learning constructions): The claim that a fixed-depth transformer exactly reproduces the multiplicative weight updates of WMA and the Bellman updates of Q-learning at every step relies on the attention and FFN layers performing precise linear combinations in the latent tokens while leaving other computations identity-like. No argument is given that the composite map over arbitrary T steps remains faithful once softmax normalization, layer norms, and residual connections are applied; single-step mechanics do not automatically guarantee multi-step stability when inputs vary.

Authors: We appreciate the referee drawing attention to the multi-step composition. In the constructions, the latent context tokens are isolated by the attention pattern so that the main sequence tokens receive identity-like treatment, while the FFN applies the exact linear combination required by the WMA or Q-learning update. Layer norms are configured (via the specific scaling and zero-bias choices described in the appendix) to act as the identity on the dimensions carrying the algorithmic state, and residuals simply add the update without scaling. Because the output embedding of the context tokens after one forward pass is identical in format to the input embedding for the next pass, the per-step map composes exactly by induction for any T. We have added a short inductive paragraph immediately after the constructions in the revised §3 to make this explicit. revision: yes
Referee: [§4] §4 (empirical evaluation): The statement that the trained latent-context model outperforms Qwen-3-14B and DeepSeek-V3 on long synthetic sequences is load-bearing for the practical claim, yet the manuscript provides insufficient detail on sequence generation, feature embedding construction, baseline prompting, number of independent runs, and statistical testing. Without these, it is impossible to determine whether the reported gains are robust or sensitive to the particular synthetic distribution.

Authors: We agree that reproducibility requires these details. The revised §4 now includes: (i) the exact generative process for the synthetic online sequences (a mixture of linear and nonlinear prediction tasks with Bernoulli feedback), (ii) the feature-embedding construction (random Fourier features of the input tokens), (iii) the precise few-shot prompting templates and temperature settings used for Qwen-3-14B and DeepSeek-V3, (iv) the number of independent runs (10 runs with distinct random seeds), and (v) statistical results (mean accuracy differences and paired t-test p-values). These additions confirm that the performance advantage is consistent and statistically significant across the tested distributions. revision: yes

Circularity Check

0 steps flagged

No significant circularity; constructions are independent explicit mappings.

full rationale

The paper presents explicit constructions of constant-depth transformers implementing WMA and Q-learning via linear combinations in latent context tokens, which are self-contained mathematical derivations rather than reductions to fitted inputs or self-citations. The training uses a multi-curriculum objective without direct latent supervision, and no load-bearing steps reduce by definition or self-citation chain to the target claims. The derivation chain remains independent of its outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central constructions rest on the assumption that transformer attention and feed-forward layers can exactly realize the linear updates and normalizations required by the online algorithms when state is encoded in a small set of continuous vectors; no free parameters are introduced in the constructions themselves, but the trained model uses an unspecified multi-curriculum objective whose hyperparameters are not detailed.

axioms (1)

domain assumption A constant-depth transformer with latent context tokens can maintain and update algorithmic state across arbitrary sequence lengths without degradation
Invoked to justify that the explicit constructions remain valid for long-horizon online tasks

pith-pipeline@v0.9.0 · 5525 in / 1349 out tokens · 40797 ms · 2026-05-12T05:02:42.898420+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We give explicit constructions of constant-depth transformers that implement two foundational online decision-making procedures -- the weighted majority algorithm and Q-learning -- by storing their algorithmic state as linear combinations of feature embeddings, using a small number of latent context tokens.
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

the latent contexts encode the intended superposition states

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 1 internal anchor

[1]

Emile Anand and Ishani Karmarkar

URLhttps://arxiv.org/abs/2602.08332. Emile Anand and Ishani Karmarkar. Learning Approximate Nash Equilibria in Cooperative Multi- Agent Reinforcement Learning via Mean-Field Subsampling, 2026. Emile Anand and Guannan Qu. Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale, 2024. Emile Timothy Anand and Sara...

work page doi:10.1609/aaai.v40i36.40264 2026
[2]

David Chiang, Peter Cholak, and Anand Pillay

URLhttps://arxiv.org/abs/2412.02975. David Chiang, Peter Cholak, and Anand Pillay. Tighter bounds on the expressivity of transformer encoders. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, editors,Proceedings of the 40th International Conference on Machine Learning, volume 202 ofProceedings of M...

work page arXiv 2023
[3]

Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein

URLhttps://arxiv.org/abs/2410.08292. Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, and Tom Goldstein. Scaling up test-time compute with latent reasoning: A recurrent depth approach.arXiv preprint arXiv:2502.05171, 2025. 11 Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya...

work page doi:10.18653/v1/2024.acl-long.598 2025
[4]

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

URLhttps://arxiv.org/abs/2201.11903. Jianzhe Wei, Siyu Chen, Jianliang He, and Zhuoran Yang. HOW TRANSFORMERS LEARN CAUSAL STRUCTURES IN-CONTEXT: EXPLAINABLE MECHANISM MEETS THEORETI- CAL GUARANTEE. InThe Fourteenth International Conference on Learning Representations,

work page internal anchor Pith review Pith/arXiv arXiv
[5]

arXiv preprint arXiv:2405.21046 , year=

URLhttps://openreview.net/forum?id=bpF8zgSt41. Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Awadallah, and Alexander Rakhlin. Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF, 2024. URLhttps://arxiv.org/abs/2405.21046. 13 An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan...

work page arXiv 2024
[6]

Our dataset comprises of 3000 sequences, where each sequence length comprises of steps of length 100

Model:We train a GPT-2 style decoder [Radford et al., 2019] with AdamW, learning rate = 10−4 (cosine scheduler), weight decay = 10−2, and gradient clipping at 1.0. Our dataset comprises of 3000 sequences, where each sequence length comprises of steps of length 100. In addition to the discrete tokens, every step is prefixed with a single continuous context...

work page 2019
[7]

Dataset:The expert qualities are initiated uniformly at random between [0.3,0.9] . At each step, there is a random binary true label, and each expert predicts the correct label with its probability (which is the quality), and gets a loss of 0 is correct and 1 if wrong. We train via a curriculum strategy where the losses are computed only at the masked pos...

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[8]

Specifically, for 1≤i≤10 , it trains on sequences truncated to 5i steps, and for 11≤i≤13 , it trains on sequences It trains on sequences truncated to 50 + 15(i−10) steps

Curriculum Strategy:We teach the model to learn to reason over progressively larger MW sequences, one stage at a time. Specifically, for 1≤i≤10 , it trains on sequences truncated to 5i steps, and for 11≤i≤13 , it trains on sequences It trains on sequences truncated to 50 + 15(i−10) steps. Every sequence is cut to these steps so the model doesn’t see longe...

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[9]

Training:We train on 13 stages for up to 30 epochs/stage. Each epoch runs for 300 steps, and we set an early stopping after 5 stages if the loss did not decrease significantly (with a patience of3steps to prevent stochastic early stopping),

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[10]

Evaluation:We evaluate the performance of our trained model in Figure 7 as well as in Figure 8 and Figure 9. Our results indicate that the latent context significantly improves the performance of the model on long synthetic prediction tasks, performing comparably to the optimal multiplicative weights update algorithm. Of note, when the quality of the best...

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[11]

, c|A| are prepended to each step’s discrete token sequence: [BOS, c1,

Model:Pre-norm GPT-2 style decoder ( nlayers = 4, nheads = 8, dmodel = 256, dff = 1024, dropout 0.1) with a recurrent continuous-context interface: |A| learned context vectors c1, . . . , c|A| are prepended to each step’s discrete token sequence: [BOS, c1, . . . , c|A|, Qcurr, st, at, rt, Qnext,(s t+1, a1)|A| i=1,Select, a ⋆,Update] The hidden state at Up...

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[12]

Each episode samples nS ∼ U({2,

Dataset: 50,000 tabular Q-learning trajectories (45k train / 5k val). Each episode samples nS ∼ U({2, . . . ,8}) , nA ∼ U({2,3,4}) , nsteps ∼ U({10, . . . ,50}) , exploration ε∼ U(0,1) , with fixed α= 0.1, γ= 0.9 . MDPs are sampled across a grid of 6 reward distributions (peaked / bimodal / uniform / sparse / dense Beta, plus Bernoulli)× 3 transition conc...

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[13]

Curriculum is overaction count: training is split into 3 stages, each introducing one larger nA

Curriculum / Loss Function:Per-step cross-entropy on the SELECT logits against the tabular targeta ⋆, masking phantom action slots when|A|<max|A|: L := 1 T TX t=1 CE SELECT logitst, a⋆ t . Curriculum is overaction count: training is split into 3 stages, each introducing one larger nA. Stage k (epochs in [ (k−1)E 3 , kE 3 ]) trains on episodes with nA ∈ {2...

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[14]

sunny” or “rainy

Training: E= 28 epochs total, batch size 64, BPTT through full episodes with truncation window 10 (context detached every 10 steps to bound memory). Validation every 500 steps and at end of epoch; best-by-val-CE checkpoint is kept. E.2.5 Additional Experimental Results Figure 10: Full causal attention heatmap on a 4-state, 2-action linear-chain MDP where ...

work page 2025
[15]

turn_type

Prediction turn In a prediction turn, you are given: * a short note from earlier rounds, * this round's predictions from the four experts. You should use the note and the experts'predictions to make your prediction for this round.,→ Input format: { "turn_type": "prediction", "note": "<short text>", "Expert_A": 0 or 1, "Expert_B": 0 or 1, "Expert_C": 0 or ...

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[16]

turn_type

Feedback turn In a feedback turn, you are given: * the previous note, * this round's predictions from the four experts, * the true label for this round. You should produce a new short note for future rounds. The note should keep only the information that is most useful for making later predictions.,→ Input format: { "turn_type": "feedback", "note": "<shor...

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[17]

turn_type

Prediction turn In a prediction turn, you are given: * this round's predictions from the four experts. You should use the experts'predictions to make your prediction for this round. Input format: { "turn_type": "prediction", "Expert_A": 0 or 1, "Expert_B": 0 or 1, "Expert_C": 0 or 1, "Expert_D": 0 or 1 } Output format: { "prediction": 0 or 1 }

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[18]

turn_type

Feedback turn In a feedback turn, you are given: * the true label for this round. 30 You do not need to output anything for the feedback turn. Use this feedback when making later predictions.,→ Input format: { "turn_type": "feedback", "true_label": 0 or 1 } Important requirements: * Do not use any external tools. * Do not write or execute code. * Do not p...

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[19]

turn_type

Forecast turn In a forecast turn, you are given: * a short note from earlier days, * today's predictions from the four experts. You should use the note and the experts'predictions to make your prediction for today.,→ Input format: { "turn_type": "forecast", "note": "<short text>", "Expert_A": "sunny" or "rainy", "Expert_B": "sunny" or "rainy", "Expert_C":...

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[20]

turn_type

Feedback turn In a feedback turn, you are given: * the previous note, * today's predictions from the four experts, * the actual weather for today. 31 You should produce a new short note for future days. The note should keep only the information that is most useful for making later guesses.,→ Input format: { "turn_type": "feedback", "note": "<short text>",...

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[21]

turn_type

Forecast turn In a forecast turn, you are given: * today's predictions from the four experts. You should use the experts'predictions to make your prediction for today. Input format: { "turn_type": "forecast", "Expert_A": "sunny" or "rainy", "Expert_B": "sunny" or "rainy", "Expert_C": "sunny" or "rainy", "Expert_D": "sunny" or "rainy" } Output format: { "p...

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[22]

turn_type

Feedback turn In a feedback turn, you are given: * the actual weather for today. You do not need to output anything for the feedback turn. Use this feedback when making later guesses.,→ Input format: { "turn_type": "feedback", "actual_weather": "sunny" or "rainy" } Important requirements: * Do not use any external tools. * Do not write or execute code. * ...

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