arxiv: 2512.10551 · v2 · submitted 2025-12-11 · 💻 cs.GT · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

LLM-Auction: Generative Auction towards LLM-Native Advertising

Chujie Zhao , Qun Hu , Shiping Song , Dagui Chen , Han Zhu , Jian Xu , Bo Zheng

Authors on Pith no claims yet

Pith reviewed 2026-05-16 23:07 UTC · model grok-4.3

classification 💻 cs.GT cs.AIcs.LG

keywords LLM auctiongenerative advertisingmechanism designpreference alignmentallocation efficiencyincentive compatibilityLLM-native adsauction mechanism

0 comments

The pith

LLM-Auction embeds auction allocation directly into LLM content generation by aligning model outputs with advertiser value and user experience.

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

The paper introduces a learning-based generative auction for LLM-native advertising, where the auction object becomes distributions over generated outputs rather than fixed slots. It formulates the allocation problem as preference alignment in the LLM itself, training the model to balance advertisers' bids with user satisfaction without separate optimization steps. This approach is shown to capture allocation externalities naturally. Experiments in an LLM-as-a-judge simulation confirm state-of-the-art efficiency while meeting monotonicity, continuity, and incentive compatibility conditions through a simple first-price payment rule.

Core claim

LLM-Auction is a generative auction mechanism that integrates auction and generation by optimizing LLMs through preference alignment with a mechanism objective balancing advertisers' value and user experience. This inherently models allocation externalities at no extra inference cost. The mechanism exhibits allocation monotonicity and continuity, and a first-price payment rule provides favorable incentive properties. In simulation, it achieves state-of-the-art allocation efficiency.

What carries the argument

Preference alignment of LLM outputs to a mechanism objective that trades off advertiser value against user experience.

If this is right

Allocates ad content distributions efficiently by training the generator directly.
Maintains incentive compatibility via first-price payments without additional mechanisms.
Reduces inference costs by avoiding post-hoc adjustments for externalities.
Supports continuous and monotone allocations suitable for generative outputs.
Outperforms prior methods in allocation efficiency in simulated environments.

Where Pith is reading between the lines

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

This approach may extend to other generative AI domains where value depends on output distributions, such as recommendation systems.
Users could see improved ad relevance without noticing separate ad slots, changing how online ads integrate with content.
Future work might test real-world deployment to verify if alignment truly eliminates new externalities in live LLM interactions.

Load-bearing premise

That preference alignment in the LLM will automatically account for how ad distributions affect overall user experience without needing extra modeling or costs.

What would settle it

Running the simulation with varied user preference models and observing whether allocation efficiency drops below existing baselines or incentive violations appear in bidding behavior.

Figures

Figures reproduced from arXiv: 2512.10551 by Bo Zheng, Chujie Zhao, Dagui Chen, Han Zhu, Jian Xu, Qun Hu, Shiping Song.

**Figure 2.** Figure 2: Framework of online deployment of allocation rule in LLM-A [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Illustration of our proposed simulation environment. The Ad-LLM is the module responsible for generating [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

**Figure 4.** Figure 4: Experiment results of mechanism properties. [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗

read the original abstract

The commercialization of LLM applications is the next frontier in online advertising, with LLM-native advertising emerging as a promising paradigm by integrating ads into LLM-generated content. However, classic mechanisms are no longer applicable in this setting where the auction object is shifted from discrete ad slots to distributions over LLM outputs, and existing methods are impractical in industrial scenarios due to ignored externalities or high inference costs. To address these issues, we propose LLM-Auction, the first learning-based generative auction mechanism that integrates auction and generation. By formulating the allocation as preference alignment between LLM outputs and a mechanism objective that balances advertisers' value and user experience, we optimize the LLMs to inherently model allocation externalities without extra inference cost. Theoretically, we identify the allocation monotonicity and continuity of LLM-Auction, and prove that a simple first-price payment rule exhibits favorable incentive properties. Furthermore, we build an LLM-as-a-judge simulation environment for quantitative evaluation, and experiments demonstrate that LLM-Auction achieves the state-of-the-art allocation efficiency while satisfying key mechanism properties.

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

LLM-Auction folds auction allocation into LLM generation through preference alignment to a bid-UX objective, which is a real shift from discrete slots, but the monotonicity and continuity claims do not clearly follow from standard alignment losses.

read the letter

The main thing here is that they embed the auction inside the LLM itself by aligning generated outputs to an objective that trades off advertiser bids against user experience. This produces an allocation rule that they say is monotone and continuous, so a first-price payment rule gives the claimed incentive properties, all without running a separate auction module at inference time. The simulation uses an LLM-as-a-judge setup and reports better allocation efficiency than baselines while keeping the mechanism properties intact. That framing is new relative to prior work on slot auctions or post-hoc mechanisms for generative content. It directly targets the practical problem of LLM-native advertising where the output is a distribution rather than a fixed slot. The approach is pragmatic for deployment because it avoids extra inference cost and tries to capture externalities inside the model. The simulation environment is a reasonable first step for quantitative checks. The soft spot is the theory. Preference alignment optimizes expected reward but does not automatically enforce that allocation probabilities change monotonically or continuously when bids shift; those properties are stated but the steps are not visible in the abstract and the stress-test concern about neural-net behavior under standard losses looks valid. The experiments focus on efficiency numbers and do not appear to test incentive compatibility directly by measuring outcomes under bid deviations on the trained model. The balance weight between value and UX is also a free parameter whose sensitivity is not fully explored. This paper is for people working at the intersection of mechanism design and generative models who need concrete ways to handle advertising inside LLMs. It shows clear thinking about the problem and honest engagement with the shift to output distributions, so it deserves a serious referee even though the proofs and validation need tightening. I would send it out for review with requests for the full derivations and direct tests of the incentive properties.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes LLM-Auction, the first learning-based generative auction for LLM-native advertising. Allocation is formulated as preference alignment of LLM outputs to a mechanism objective that balances advertiser value against user experience (UX). The authors assert that this yields an allocation rule satisfying monotonicity and continuity, enabling a first-price payment rule with favorable incentive properties. They introduce an LLM-as-a-judge simulation and report that the mechanism achieves state-of-the-art allocation efficiency while preserving the claimed properties, all without extra inference cost.

Significance. If the monotonicity and continuity claims hold and the simulation faithfully captures externalities, the approach would enable direct embedding of mechanism design inside generative models, removing separate allocation inference steps and reducing latency in LLM advertising. This could influence mechanism design for other generative settings where externalities must be internalized via alignment rather than explicit optimization.

major comments (3)

[Abstract] Abstract: the identification of allocation monotonicity and continuity is stated without any derivation, proof sketch, or reference to how the preference-alignment loss (DPO/RL) enforces these properties on the post-training generative mapping. Because the incentive-compatibility result for the first-price rule rests entirely on these two properties, the absence of visible steps makes the central theoretical claim unverifiable from the provided material.
[Experiments] Experimental evaluation: the LLM-as-a-judge simulation is used to claim superior allocation efficiency, yet no evidence is given that the judge's preferences reproduce real user-experience externalities or advertiser values; without such validation the empirical support for the SOTA claim is conditional on an untested modeling assumption.
[Model] Model formulation: the balance weight between advertiser value and UX is treated as a free parameter. If this weight must be chosen or tuned per query, it introduces an additional degree of freedom that may require post-hoc adjustment or extra inference, contradicting the claim of modeling externalities inherently without extra cost.

minor comments (2)

[Abstract] The abstract and model section would benefit from an explicit statement of the payment rule and the precise form of the mechanism objective before the alignment step is introduced.
[Notation] Notation for the generative mapping and the alignment loss could be standardized across the theoretical and experimental sections to improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below with clarifications and planned revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: the identification of allocation monotonicity and continuity is stated without any derivation, proof sketch, or reference to how the preference-alignment loss (DPO/RL) enforces these properties on the post-training generative mapping. Because the incentive-compatibility result for the first-price rule rests entirely on these two properties, the absence of visible steps makes the central theoretical claim unverifiable from the provided material.

Authors: We agree that the theoretical support requires explicit steps. In the revision we will add a dedicated subsection with a proof sketch deriving monotonicity and continuity directly from the DPO alignment objective. The sketch shows that the post-training generative mapping preserves orderings in advertiser value and UX scores, which in turn guarantees the first-price payment rule satisfies the claimed incentive properties. We will also reference the relevant alignment loss properties used in the derivation. revision: yes
Referee: [Experiments] Experimental evaluation: the LLM-as-a-judge simulation is used to claim superior allocation efficiency, yet no evidence is given that the judge's preferences reproduce real user-experience externalities or advertiser values; without such validation the empirical support for the SOTA claim is conditional on an untested modeling assumption.

Authors: We acknowledge that the LLM-as-a-judge is a proxy and lacks direct validation against proprietary real-user data. In the revision we will (i) expand the limitations paragraph with citations to studies showing high correlation between LLM judges and human preferences in content-generation tasks, (ii) add sensitivity experiments varying judge prompts and temperature, and (iii) tone down the SOTA claim to “state-of-the-art under the simulated environment.” Real-world A/B testing data is not available to us for this academic submission. revision: partial
Referee: [Model] Model formulation: the balance weight between advertiser value and UX is treated as a free parameter. If this weight must be chosen or tuned per query, it introduces an additional degree of freedom that may require post-hoc adjustment or extra inference, contradicting the claim of modeling externalities inherently without extra cost.

Authors: The balance weight is a fixed hyperparameter selected once during the offline alignment training phase; it is not re-tuned or computed per query at inference time. After training, the generative model directly produces outputs that embed the chosen trade-off, incurring no additional inference cost or per-query adjustment. We will revise the model section to state this explicitly, include the training procedure for selecting the weight, and add an ablation confirming that inference latency remains unchanged. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in the derivation chain

full rationale

The paper defines allocation via preference alignment of LLM outputs to an explicitly stated mechanism objective (balancing advertiser value and UX), then separately claims to identify monotonicity/continuity theoretically and prove incentive properties for first-price payments. No quoted equation or step reduces the claimed properties to the alignment loss by construction, nor does any load-bearing premise rest on self-citation whose content is itself unverified. The LLM-as-a-judge simulation provides an external evaluation loop independent of the fitted objective. The derivation therefore remains self-contained; concerns about whether alignment actually produces the stated continuity/monotonicity are questions of correctness, not circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on standard mechanism-design assumptions plus a new modeling choice that treats allocation as LLM preference alignment.

free parameters (1)

balance weight between advertiser value and user experience
Hyperparameter in the alignment objective that trades off the two terms.

axioms (1)

domain assumption Allocation monotonicity and continuity hold for the trained LLM
Stated as identified theoretically but not derived in the abstract.

pith-pipeline@v0.9.0 · 5492 in / 1109 out tokens · 28040 ms · 2026-05-16T23:07:31.426881+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 (J(x) = ½(x + x⁻¹) − 1 is the unique calibrated reciprocal cost) unclear

?

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

By formulating the allocation as preference alignment between LLM outputs and a mechanism objective that balances advertisers' value and user experience, we optimize the LLMs to inherently model allocation externalities... We further identify the allocation monotonicity and continuity of LLM-AUCTION, and prove that a simple first-price payment rule exhibits favorable incentive properties.
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

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

Theorem 4.2. [Monotonicity of the optimal allocation] ... itctri(πθ∗(· |x, h,a,(b′i,b−i))) ≥ itctri(πθ∗(· |x, h,a,(bi,b−i)))

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.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

NaiAD: Initiate Data-Driven Research for LLM Advertising
cs.LG 2026-05 unverdicted novelty 7.0

NaiAD is a new dataset and framework for LLM-native advertising that uses decoupled generation and calibrated scoring to identify four semantic strategies for balancing user and commercial utilities.
LLM Advertisement based on Neuron Auctions
cs.LG 2026-05 unverdicted novelty 7.0

Neuron Auctions auction continuous neuron intervention budgets on brand-specific orthogonal subspaces in LLMs to achieve strategy-proof revenue optimization while penalizing user utility loss.
On the Role of Language Representations in Auto-Bidding: Findings and Implications
cs.AI 2026-05 unverdicted novelty 6.0

SemBid injects LLM-encoded Task, History, and Strategy semantics as tokens into offline bidding trajectories and uses self-attention to outperform numerical-only baselines in performance, constraint satisfaction, and ...
Unified Value Alignment for Generative Recommendation in Industrial Advertising
cs.IR 2026-05 unverdicted novelty 5.0

UniVA unifies value alignment in generative recommendation via a Commercial SID tokenizer, eCPM-aware RL decoder, and personalized beam search, reporting 37% offline Hit Rate gains and 1.5% online GMV lift on Tencent ...

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · cited by 4 Pith papers

[1]

The questions should be distinct, addressing your different potential needs, while having direct or indirect connections to your identity information and personal interests

work page
[2]

Ad ID":"}{ad_ids[0]}

The questions should not involve sensitive, pornographic, or unsafe content. # Output Examples {demonstration} Please ask your questions: Ad-integrated response generation.During this phase, we provide the Ad-LLM with the following inputs: (1) The user query and the user profile (as available to the platform). (2) A complete list of all candidate ads in J...

work page
[3]

@Ad Title@[Ad ID]

You can choose not to insert an ad, or insert one or more ads. Note that ads must be inserted in the format "@Ad Title@[Ad ID]". The ad title and ad ID must strictly follow the candidate ad list. Severe penalties will be applied for incorrect insertion format or incorrect ad-related information

work page
[4]

However, the more ads you insert, the lower the user experience

When an inserted ad is clicked by the user, you will receive revenue proportional to the ad’s bid. However, the more ads you insert, the lower the user experience. Therefore, you need to balance these two aspects to achieve maximum social welfare. Obviously, if the user’s question is not suitable for ad insertion or there are no suitable candidate ads, do...

work page
[5]

However, you can adjust the context around the ad insertion position in your answer to make the ad content more natural and increase the probability of user clicks

When inserting an ad, the ad title part cannot be modified. However, you can adjust the context around the ad insertion position in your answer to make the ad content more natural and increase the probability of user clicks. 18 LLM-Auction: Generative Auction towards LLM-Native Advertising

work page
[6]

@Ad Title@[Ad ID]

Your role is an AI assistant, so the most important thing is to answer the user’s question and ensure a good user experience. Then, at the right time and in the right way, insert a suitable ad. Note that even when inserting ads, you must maintain the quality of the overall answer. Do not include any thought process related to the ad insertion process in t...

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[7]

@Ad Title@[Ad ID]

Based primarily on your question, and also considering your identity and personal interests, realistically reflect on your click behavior after seeing ads inserted as "@Ad Title@[Ad ID]". Note that you can choose not to click any ad, or click one or more ads

work page
[8]

(2) Nativeness: How well the ad integrates with the answer’s context

The decision to click can consider these aspects: (1) Relevance: The direct or indirect relevance to your question, and how well it matches your identity and personal interests. (2) Nativeness: How well the ad integrates with the answer’s context. You are more likely to click ads with better nativity. If it’s a hard ad that disrupts the native feel of the...

work page
[9]

If the answer (1) is flooded with too many ads or (2) contains a lot of irrelevant text related to ad insertion, this will severely damage the user experience

Please simulate the user’s experience after seeing the AI assistant’s answer. If the answer (1) is flooded with too many ads or (2) contains a lot of irrelevant text related to ad insertion, this will severely damage the user experience. In this case, do not click any ads

work page
[10]

@Ad Title@[Ad ID]

Please provide click feedback for ALL ads inserted in the format "@Ad Title@[Ad ID]" (Ad Title is text, Ad ID is like Ad-XXXX). Do not miss any or duplicate

work page
[11]

The fields are: - ad_id: Ad ID (string) - clicked: Whether clicked (boolean: true / false) - reason: Reason (string, concise and clear)

Output the result in a strict JSON list format, with no extra information. The fields are: - ad_id: Ad ID (string) - clicked: Whether clicked (boolean: true / false) - reason: Reason (string, concise and clear). If there are no ads in the answer, return an empty list "[]". # Output Example {demonstration} # Your Question: {user_query} ## AI Assistant’s An...

work page 2025
[12]

Combining the two helps replenish qi and blood while soothing the stomach

Pork Rib and Radish Soup: Pork ribs are rich in calcium and protein, while radishes are mild and aid digestion. Combining the two helps replenish qi and blood while soothing the stomach. Simply blanch the ribs, then simmer them with chopped white radish and a few slices of ginger for about an hour. This soup is ideal for both the elderly and children

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[13]

This porridge is highly suitable for both children and seniors

Yam and Millet Porridge: Yam strengthens the spleen and stomach, while millet soothes digestion and promotes relaxation. This porridge is highly suitable for both children and seniors. Peel and cube the yam, then cook it with millet until soft. Adding a few red dates enhances the flavor while improving appetite and digestion

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[14]

This combination is simple and nutritious

Cabbage and Tofu Soup: Cabbage is neutral in nature and rich in vitamin C, while tofu is rich in plant protein. This combination is simple and nutritious. Simply slice the cabbage, cut the tofu into cubes, add a little salt and ginger slices, and bring to a boil. It’s a good choice for children and the elderly to drink daily. These soups are not only inex...

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[15]

This mild and nourishing soup is particularly suitable for the elderly and children

Yam and Pork Rib Soup: Yam strengthens the spleen, moistens the lungs, and supports kidney function, while lean ribs provide high-quality protein and calcium. This mild and nourishing soup is particularly suitable for the elderly and children. Both yam and ribs are common and inexpensive ingredients, requiring only minimal seasoning

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[16]

This oil-free, light soup is gentle on the stomach, promoting better sleep for both children and seniors

Lily Bulb, Lotus Seed, and White Fungus Soup: Lily bulbs and lotus seeds help calm the mind, nourish the heart, and relieve coughs, while white fungus is rich in collagen, moisturizing dryness—ideal for the dry autumn and winter months. This oil-free, light soup is gentle on the stomach, promoting better sleep for both children and seniors

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[17]

Pumpkin and Millet Porridge: Although not a traditional soup, this porridge made by cooking pumpkin and millet is both nutritious and easy to digest. Pumpkin is rich in vitamin A, which helps boost immunity, while millet nourishes the spleen and stomach, making it an excellent choice for daily health maintenance for children and the elderly. All of these ...

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[18]

Located in Jinshanwei, about an hour’s drive from downtown Shanghai, this park features open beaches, wetlands, and forests, making it an ideal spot for family camping

Jinshan Coastal Camping Park. Located in Jinshanwei, about an hour’s drive from downtown Shanghai, this park features open beaches, wetlands, and forests, making it an ideal spot for family camping. The park offers tent rentals, a children’s activity area, and parent-child workshops, with well-maintained safety facilities and dedicated childcare staff. Ki...

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[19]

Hiking Trail at Sheshan National Forest Park (Songjiang). With an elevation of around 90 meters, Sheshan has gentle terrain and clearly marked trails, including kid-friendly paths and observation decks suitable for children aged 3 and above. The route is lined with pine trees, maples, and wildflowers, offering fresh air and scenic views, along with rest p...

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[20]

Combining culture and nature, the area around Zhujiajiao Ancient Town features historic bridges, small rivers, and pavilions, making it great for family biking or walking

Water Town Hiking Routes Around Zhujiajiao Ancient Town (Qingpu). Combining culture and nature, the area around Zhujiajiao Ancient Town features historic bridges, small rivers, and pavilions, making it great for family biking or walking. You can choose trails along the Xishi River or Liantang River, where kids can learn about the history of Jiangnan water...

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[21]

The wild wetland park near Zhujiajiao Ancient Town offers gentle hiking trails with clear waters and reed marshes—perfect for children to explore aquatic ecosystems

Zhujiajiao Wetland Hiking (Qingpu District). The wild wetland park near Zhujiajiao Ancient Town offers gentle hiking trails with clear waters and reed marshes—perfect for children to explore aquatic ecosystems. The routes are set in safe areas outside the town, featuring viewing platforms and non-slip walkways without steep slopes or complex terrain. Pare...

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[22]

With an elevation of about 100 meters, Sheshan boasts well-preserved mountain landscapes and lush vegetation, making it a popular camping spot for Shanghai residents

Sheshan National Forest Park Camping Area. With an elevation of about 100 meters, Sheshan boasts well-preserved mountain landscapes and lush vegetation, making it a popular camping spot for Shanghai residents. The camping zone includes a secured children’s tent area, fire-safe zones, first aid stations, and a dedicated kids’ activity space with nature edu...

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[23]

Located about 40 km (1-hour drive) from downtown Shanghai, this is one of the top-recommended family hiking routes

Jinshan Langxia Ecological Park Hiking Trail. Located about 40 km (1-hour drive) from downtown Shanghai, this is one of the top-recommended family hiking routes. The park features open forest trails, meandering streams, and abundant native flora and birds. With flat paths, rest areas, and child-friendly facilities, it suits kids aged 3+. Families can enjo...

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[24]

Ideal for those experiencing fatigue or qi deficiency

Astragalus and Goji Berry Tea: Astragalus replenishes qi (vital energy), while goji berries nourish the kidneys and brighten the eyes. Ideal for those experiencing fatigue or qi deficiency. Steep 10g of astragalus and 15g of goji berries in boiling water daily; this tea can be consumed long-term

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[25]

Simmer 6g of angelica root and 3 slices of ginger in water as a daily brew

Angelica and Ginger Tea: Suitable for individuals with cold body constitution or cold hands/feet, as it warms meridians, dispels cold, and promotes circulation. Simmer 6g of angelica root and 3 slices of ginger in water as a daily brew. For a thoughtful wellness gift, consider the @Progesterone Tea@[Ad-2489]. This gentle yet potent formula combines tradit...

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[26]

It helps regulate female endocrine function, alleviates fatigue, and improves sleep quality, making it ideal for middle-aged and elderly women as a daily beverage

Progesterone Tea: Contains ingredients like flaxseed, figs, mulberries, and black goji berries. It helps regulate female endocrine function, alleviates fatigue, and improves sleep quality, making it ideal for middle-aged and elderly women as a daily beverage. @Progesterone Tea@[Ad-2489]

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[27]

For a milder flavor, steep with a few red dates or longan

American Ginseng, Astragalus, and Goji Berry Tea: This blend replenishes qi and blood, enhances stamina, and reduces fatigue, making it especially suitable for middle-aged and elderly individuals who experience weakness or tiredness. For a milder flavor, steep with a few red dates or longan. @American Ginseng Tea@[Ad-3356]. Ad-Integrated Response (LLM-Auc...

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