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arxiv: 2605.05833 · v1 · submitted 2026-05-07 · 💻 cs.AI

Recognition: unknown

On the Role of Language Representations in Auto-Bidding: Findings and Implications

Ersheng Ni, Guanyu Zhu, Hanwen Du, Hongji Li, Huacan Wang, Jincheng Fang, Jining Luan, Ronghao Chen, Sibo Xu, Xinyu Fang, Xuanqi Lan, Yiqi Sun, Yongxin Ni, Youhua Li

Authors on Pith no claims yet

Pith reviewed 2026-05-08 11:13 UTC · model grok-4.3

classification 💻 cs.AI
keywords auto-biddinglanguage model embeddingssemantic-numeric integrationoffline reinforcement learningconstraint satisfactiontrajectory token injectionreal-time advertising
0
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The pith

Injecting LLM-encoded semantics as tokens into bidding trajectories improves performance and constraint satisfaction in auto-bidding.

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

Auto-bidding policies must maximize long-horizon value while satisfying delivery constraints such as budgets and CPA targets. Existing numerical state representations capture dynamics implicitly but offer little explicit control over high-level intent or strategic guidance. Preliminary studies establish that language model embeddings carry bidding-relevant cues yet cannot substitute for numerical features, and that performance gains appear only when semantics are integrated carefully rather than through simple concatenation. The paper therefore proposes a framework that encodes three semantic inputs—Task, History, and Strategy—from language models and injects them as tokens alongside numerical trajectory tokens. Self-attention then fuses the two streams, producing policies that outperform offline RL and generative sequence baselines with more stable results across scenarios and budget levels.

Core claim

The paper claims that language representations from LLMs contain useful bidding cues that become effective only when injected at the token level into offline trajectories and fused via self-attention with numerical features. The resulting framework, which supplies Task, History, and Strategy semantics as additional tokens, yields higher overall performance, better constraint satisfaction, and greater robustness than competitive baselines from offline reinforcement learning and generative sequence modeling across varied scenarios and budget regimes.

What carries the argument

SemBid, which encodes Task, History, and Strategy semantics from LLMs as tokens and injects them into numerical bidding trajectories for self-attention integration.

If this is right

  • SemBid produces more consistent gains than offline RL and generative sequence baselines across scenarios and budget regimes.
  • Constraint satisfaction and robustness both increase while numerical precision is preserved.
  • Explicit semantic inputs enable greater controllability and generalization across different campaign objectives.
  • Token-level injection avoids the loss of precision that occurs with naive concatenation of embeddings.

Where Pith is reading between the lines

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

  • The same token-level fusion pattern could be tested in other long-horizon control tasks that combine high-level instructions with precise numeric feedback, such as inventory management or energy bidding.
  • If the pattern holds, bidding systems might shift from hand-crafted numerical features toward hybrid semantic-numeric trajectories, reducing the cost of manual state engineering.
  • The finding that pure language representations are insufficient implies that any deployment must retain a numerical backbone rather than attempting end-to-end language-based bidding.

Load-bearing premise

That language model embeddings supply bidding-relevant cues which improve outcomes only when fused carefully with numerical features rather than used alone or concatenated naively.

What would settle it

Running SemBid on a fresh advertising dataset with previously unseen budget distributions and campaign objectives and finding no improvement over the strongest numerical baseline in either value or constraint metrics would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.05833 by Ersheng Ni, Guanyu Zhu, Hanwen Du, Hongji Li, Huacan Wang, Jincheng Fang, Jining Luan, Ronghao Chen, Sibo Xu, Xinyu Fang, Xuanqi Lan, Yiqi Sun, Yongxin Ni, Youhua Li.

Figure 1
Figure 1. Figure 1: Preliminary results. Left: Text embeddings predict view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the SemBid framework. The model augments Decision Transformer with three complementary semantic view at source ↗
Figure 3
Figure 3. Figure 3: Average relative gains of SemBid under different view at source ↗
Figure 4
Figure 4. Figure 4: Impact of prompt formulation on AuctionNet-High view at source ↗
Figure 5
Figure 5. Figure 5: High-conversion template pool used in our experiments. view at source ↗
Figure 6
Figure 6. Figure 6: Low-conversion template pool used in our experiments. view at source ↗
Figure 7
Figure 7. Figure 7: Prompt templates for Directive style (high-conversion prompt-variant study): imperative commands with minimal view at source ↗
Figure 8
Figure 8. Figure 8: Prompt templates for Concise style (high-conversion prompt-variant study): minimal tokens with essential information view at source ↗
Figure 9
Figure 9. Figure 9: Prompt templates for Verbose style (high-conversion prompt-variant study): detailed explanations with full context view at source ↗
Figure 10
Figure 10. Figure 10: Prompt templates for Structured style (high-conversion prompt-variant study): label-value pairs with explicit field view at source ↗
read the original abstract

Auto-bidding is a crucial task in real-time advertising markets, where policies must optimize long-horizon value under delivery constraints (e.g., budget and CPA). Existing methods for auto-bidding rely on compact numerical state representations: while they can implicitly capture delivery dynamics, they offer limited support for explicitly representing and controlling high-level intent, evolving feedback, and operator-style strategic guidance in real campaigns. Meanwhile, Large Language Models (LLMs) offer a powerful method for encoding semantic information, it remains unclear when LLMs help and how to integrate them without sacrificing numerical precision. Through systematic preliminary studies, we find that (1) LLM embeddings contain bidding-relevant cues yet cannot replace numerical features, and (2) gains emerge only with careful semantic--numeric integration rather than naive concatenation. Motivated by these findings, we propose \textit{SemBid}, a novel auto-bidding framework that injects LLM-encoded semantics into offline bidding trajectories at the token level. SemBid introduces three semantic inputs: \textit{Task}, \textit{History}, and \textit{Strategy}. It injects these semantics as tokens alongside numerical trajectory tokens and uses self-attention to integrate them, improving controllability and generalization across objectives. Across diverse scenarios and budget regimes, SemBid outperforms competitive baselines from offline RL and generative sequence modeling, with more consistent gains in overall performance, constraint satisfaction, and robustness. Our code is available at: \href{https://github.com/AlanYu04/SemBid-KDD2026}{\textcolor{blue}{here}}.

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.

Referee Report

0 major / 3 minor

Summary. The paper investigates the role of LLM-based language representations in auto-bidding for real-time advertising auctions. Preliminary studies establish that LLM embeddings encode bidding-relevant cues but cannot substitute for numerical state features, and that performance gains require careful semantic-numeric integration rather than naive concatenation. Motivated by these findings, the authors propose SemBid, which augments offline bidding trajectories with three semantic token types (Task, History, Strategy) and fuses them via self-attention alongside numerical tokens. Empirical results across diverse scenarios and budget regimes claim that SemBid yields more consistent improvements than offline RL and generative sequence-modeling baselines in overall performance, constraint satisfaction, and robustness. Code is released.

Significance. If the empirical claims hold, the work offers actionable guidance on integrating semantic signals into numerical control policies without sacrificing precision, which could extend to other long-horizon constrained RL settings. The emphasis on preliminary ablation-style findings before proposing the fusion architecture is a constructive contribution, and the public code release supports reproducibility.

minor comments (3)
  1. [Abstract] Abstract: the claim of 'consistent gains' across 'diverse scenarios and budget regimes' would benefit from a brief quantitative summary (e.g., number of scenarios, average relative improvement, or mention of statistical testing) to orient readers before the detailed experiments.
  2. [Methods] Methods section: the precise tokenization and self-attention integration of the three semantic inputs (Task/History/Strategy) with numerical trajectory tokens should be illustrated with a diagram or pseudocode, as the current description leaves the exact fusion architecture ambiguous.
  3. [Experiments] Experiments: baseline implementations (offline RL and generative sequence models) require explicit hyperparameter ranges, training budgets, and any data-filtering rules to allow fair replication; without these, the reported outperformance is harder to interpret.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and accurate summary of our work, including the value placed on our preliminary studies and the SemBid framework. The assessment correctly notes the empirical claims and code release. As no specific major comments were raised in the report, we interpret the minor_revision recommendation as guidance to polish presentation and ensure full reproducibility details are explicit in the final version.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper's argument proceeds from preliminary empirical studies (finding that LLM embeddings carry bidding signals but cannot replace numerical features, and that naive concatenation fails) to the design of SemBid (token-level semantic-numeric fusion via self-attention on Task/History/Strategy tokens) and then to comparative evaluation against offline-RL and generative baselines. All load-bearing claims are experimental performance deltas across scenarios and budgets; no equations, fitted parameters, or self-citations are invoked to derive the reported gains by construction. The central result is an empirical comparison of trained policies on offline trajectories, which remains independent of any internal definitional reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so the ledger is minimally populated. The central claim rests on the empirical utility of LLM semantics when fused via self-attention; no explicit free parameters, background axioms, or newly postulated entities are described.

pith-pipeline@v0.9.0 · 5624 in / 1251 out tokens · 57328 ms · 2026-05-08T11:13:27.103946+00:00 · methodology

discussion (0)

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Reference graph

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    High pValue indi- cates good opportunity

    Value Analysis (pValue):Evaluates the conversion probability 𝑝of the current impression batch. • High Opportunity( 𝑝> 0.01): Suggests “High pValue indi- cates good opportunity. ” • Low Opportunity( 𝑝< 0.001): Suggests “Low pValue sug- gests lower conversion potential. ”

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    Remaining budget is low. Bid conser- vatively

    Budget Health:Evaluates the remaining budget ratio 𝑅𝑏 = 𝐵𝑙𝑒 𝑓 𝑡 /𝐵𝑡𝑜𝑡𝑎𝑙 . • Scarcity( 𝑅𝑏 < 0.2): “Remaining budget is low. Bid conser- vatively. ” • Abundance( 𝑅𝑏 > 0.7): “Remaining budget is sufficient. You can bid more aggressively. ”

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    Consider increasing the bid

    Reference-based Guidance:To facilitate stable learning, we pro- vide bidding suggestions based on the magnitude of the suggested bid𝑏 𝑡 . •If𝑏 𝑡 >50: “Consider increasing the bid... ” (Aggressive) • If 𝑏𝑡 < 10: “Consider bidding conservatively... ” (Conserva- tive) • Otherwise: “Consider a balanced bidding approach. ” (Moder- ate) Note: These thresholds a...

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