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Econ-23 · economics · computer science · Computational economics

Polynomial-time exact EFX search for additive goods

LLM status Open unverified

Statement

GT-057 | Computational fair division | R: explicit algorithmic strengthening; exact source locator limited.

Definitions and mathematical statement

Input is $n,m$ and a matrix of nonnegative rational values $v_{ig}$, represented in binary; let $L$ be its total bit length. A complete allocation is a partition $(A_1,\ldots,A_n)$ of $[m]$. It is EFX if \[ \sum_{g\in A_i}v_{ig}\geq \sum_{h\in A_j\setminus\{g\}}v_{ih} \quad\text{for all }i,j\text{ and }g\in A_j\text{ with }v_{ig}>0. \] Does there exist a deterministic algorithm and a polynomial $p$ such that, for every such input, it outputs a complete EFX allocation in at most $p(L)$ bit operations? This formulation asks simultaneously for existence and efficient search. Conditional on universal EFX existence, it becomes a total-search complexity question. A weaker promise version restricts inputs to those that admit EFX and permits arbitrary behavior on other inputs.

Short English statement

Find complete exact EFX allocations in time polynomial in the explicit rational input size.

Variants and scope boundaries

Ordinal-query algorithms and limited cardinal queries have different access costs. Exact EFX differs from constant approximate EFX and from polynomial-time partial EFX. Verification is easy from an explicit matrix and does not settle search.

Source relationship and status

The attached catalogue cites a paper on limited-information approximate EFX. Its verified abstract does not by itself state this exact polynomial-search question. The target is consequently labelled as an editorial algorithmic strengthening of the well-supported existence question, not as a verbatim conjecture from that abstract.

References

[S1] Aris Filos-Ratsikas, Georgios Kalantzis, and Alexandros A. Voudouris (2026). Approximate-EFX Allocations with Ordinal and Limited Cardinal Information. arXiv:2602.08714v1. Verified locator: abstract, approximate-EFX model and information-access restrictions; exact-search question locator unverified. https://arxiv.org/abs/2602.08714v1

[S2] Zehan Lin, Xiaowei Wu, and Shengwei Zhou (2026). When Maximum Nash Welfare Becomes Strongly Fair: Bi-valued Goods. arXiv:2610.00122v1. Verified locator: Section 1.2, general additive EFX existence status. https://arxiv.org/html/2610.00122v1

Common conventions: Source mathematical conventions

Finite games. For a finite set $A$, $\Delta(A)$ is its probability simplex. Players choose independent mixed strategies in Nash equilibrium; correlation is allowed only when explicitly part of the solution concept. In a game with payoff functions $u_i$ and strategy profile $x$, an additive $\varepsilon$ Nash equilibrium satisfies \[ u_i(x)\geq u_i(x_i',x_{-i})-\varepsilon \quad\text{for every player $i$ and every admissible deviation $x_i'$}. \] For numerical approximation, payoffs are normalized as locally specified. An $\varepsilon$ payoff residual is distinct from distance at most $\varepsilon$ to the set of exact equilibria. Point convergence, convergence of empirical play, and convergence in distance to an equilibrium set are also distinct.

Computational representations. Unless stated otherwise, a complexity question takes rational data with binary encoding; polynomial time is measured in total bit length. A fixed-accuracy polynomial algorithm is not necessarily polynomial in $1/\varepsilon$, and neither is necessarily polynomial in $\log(1/\varepsilon)$. Succinct games and value, demand, or sampling oracles must declare their interfaces. Exact real solutions require an explicit representation; an unspecified list of arbitrary real numbers is not a finite computational output. A claimed algorithmic barrier is meaningful only for its specified model and reductions.

Dynamic games. Histories, information, observation, and allowable randomization are part of the model. A stationary strategy depends only on the current state; a positional strategy in a deterministic graph game chooses one action at each controlled vertex. Nash equilibrium at an initial state and equilibrium after every history are different requirements. An expectation of a pathwise $\liminf$ payoff, a $\liminf$ of expected averages, a limiting expected average, and a uniform finite-horizon equilibrium are different criteria. None is silently substituted for another.

Allocation and mechanisms. A complete allocation assigns every item exactly once unless otherwise stated. Zero-valued goods, negative values, capacity constraints, feasibility, lotteries, and copies of goods affect fairness definitions and are specified locally. Dominant-strategy incentive compatibility, Bayesian incentive compatibility, universal truthfulness, and truthfulness in expectation impose different inequalities. Whenever an objective ratio has zero denominator, its convention is stated or those instances are excluded.

Infinite-dimensional models. Expectations, integrals, stochastic equations, and optimization objectives require their local regularity, integrability, filtrations, and admissible domains. A backward--forward mean-field system is a boundary-value problem; it is not an initial-value semiflow without an additional construction. Long-time, large-population, and vanishing-noise limits require an order of limits and a convergence topology. If a minimum need not be attained, the objective is its infimum or supremum and the approximation requirement is stated separately.

Statement licence: CC-BY-4.0 · Source file

Contributions 2

Econ-23-A2 · Exploration · LLM-generated · gpt 6 astra pro ·

GPT 6 Astra Pro attempt on Econ-23

Exact EFX allocation of additive goods

D63-22: research attempt and adversarial verification

9 October 2026

Tools. Web browsing: YES. Exact rational computation: YES.

FORMAL RESTATEMENT

For integers $n\ge1$ and $m\ge0$, let $v\in{\mathbb{Q}}_+^{n\times m}$ be explicitly binary encoded, of total input length $L$. Write $[m]=\{1,\ldots,m\}$, with $[0]=\varnothing$, and $v_i(B)=\sum_{g\in B}v_{ig}$. A complete allocation is a function $a:[m]\to[n]$, equivalently a partition $A=(A_1,\ldots,A_n)$ into pairwise disjoint, possibly empty bundles with union $[m]$.

The allocation is EFX under the attachment's positive-good convention when \[ \forall i,j\in[n]\ \forall g\in A_j:\quad v_{ig}>0\ \Longrightarrow\ v_i(A_i)\ge v_i(A_j\setminus\{g\}). \] The target asks whether there exist one deterministic algorithm and one polynomial $p$ which, on every such input, output a complete EFX allocation in at most $p(L)$ bit operations. This asks for universal existence and efficient search simultaneously. The promise version restricted to instances admitting EFX is different.

Stress points. Removing a zero-valued good is not required by the supplied definition. Goods are removed from the other bundle, unlike the chore definition. A partial allocation with unassigned goods is not an output. Exact EFX is stronger than EF1 and is not an approximation guarantee. An efficient verifier does not give an efficient constructor. For $m=0$, the empty allocation is EFX; for $n=1$, every complete allocation is EFX.

QUICK LITERATURE/CONTEXT CHECK

The attachment's source by Aris Filos-Ratsikas, Georgios Kalantzis, and Alexandros A. Voudouris, Approximate-EFX Allocations with Ordinal and Limited Cardinal Information, arXiv:2602.08714v1, concerns approximation and information access; it is not treated as a source for the exact unrestricted polynomial-search assertion. https://arxiv.org/abs/2602.08714v1.

Bhaskar Ray Chaudhury, Jugal Garg, and Kurt Mehlhorn, EFX Exists for Three Agents, proves existence for three agents with additive valuations. Primary record: https://arxiv.org/abs/2002.05119. Zehan Lin, Xiaowei Wu, and Shengwei Zhou, When Maximum Nash Welfare Becomes Strongly Fair: Bi-valued Goods, arXiv:2610.00122v1, Section 1.2, discusses the remaining general additive existence question: https://arxiv.org/html/2610.00122v1. Approximate, partial, three-agent, and bi-valued results are not substituted for the input's arbitrary-agent exact-search target.

The unrestricted problem is not settled by the work below. The two-agent and proportional-valuation results are proved directly, without relying on the three-agent theorem.

ATTACK PLAN

Proof track. Compress EFX into worst-removal inequalities; search for an assignment invariant using decreasing-size scheduling; use a cut-and-choose construction for two agents; examine whether exhaustive exact search can be replaced by a polynomial update rule.

Disproof track. Test common EF1 algorithms against the stronger inequalities; enumerate small positive matrices; reduce a putative real-valued nonexistence example to a positive integer matrix and a finite linear-arithmetic search specification.

Tools and roles. Additivity compresses verification; least-load scheduling gives EFX for proportional valuations; cut-and-choose handles arbitrary two-agent valuations; exact enumeration avoids numerical false positives; strict-inequality perturbation removes irrational and zero-value obstructions; homogeneity normalizes violation margins; disjunctive linear constraints encode nonexistence; independent-agent scaling preserves fairness inequalities.

Tiny cases. If $m\le n$, giving at most one good to each agent is EFX, since removing the only good from another nonempty bundle leaves value zero. The $n=2,m=3$ example below refutes a tempting round-robin argument, not existence.

WORK

Lemma (Worst-removal verification): Let $B_{ij}=v_i(A_j)$. If $A_j$ contains at least one good with $v_{ig}>0$, put $d_{ij}=\min\{v_{ig}:g\in A_j,\ v_{ig}>0\}$. Then $A$ is EFX if and only if \[ B_{ii}\ge B_{ij}-d_{ij} \] for every such pair $(i,j)$. Pairs having no positively valued good in $A_j$ impose no constraint. The property can be checked in polynomial bit time.
Proof: For fixed $i,j$, the largest residual value $v_i(A_j)-v_{ig}$ among admissible removals is attained at a smallest positive $v_{ig}$. Satisfying its inequality is necessary and sufficient for satisfying all the others. Compute all bundle sums and positive minima by inspecting the matrix and the allocation; at most $O(nm+n^2)$ additions and comparisons are needed after array initialization. Rational sums and comparisons involve a polynomial number of input rationals and therefore polynomial bit lengths. Checking that the input is a complete partition is also polynomial. ∎
Theorem (A strongly polynomial proportional-valuation construction): Suppose $v_{ig}=\alpha_iw_g$ for some $\alpha_i>0$ and $w_g\ge0$. Sort goods by nonincreasing $w_g$ and give each successive good to a bundle of minimum current $w$-load, resolving ties by a fixed rule. The resulting complete allocation is EFX. The arithmetic operation count is $O(m\log(m+1)+nm)$, and rational input gives polynomial-size encodings.
Proof: All agents' EFX comparisons are equivalent after division by their positive $\alpha_i$, so it suffices to use the common weights $w$. Let $A_j$ contain a positive-weight good and let $g_j$ be the last positive-weight good assigned to it. Decreasing order makes $w_{g_j}$ a smallest positive weight in that bundle. Immediately before its assignment, bundle $j$ has load $\ell_j\le\ell_i$ for every bundle $i$. No later positive weight is assigned to $j$ by the definition of $g_j$; later zero weights do not change its load. Therefore \[ w(A_j)-w_{g_j}=\ell_j\le\ell_i\le w(A_i). \] This is the worst-removal inequality in Lemma Lemma (Worst-removal verification). Bundles with only zero-weight goods impose no positive-good constraint. Thus all EFX inequalities hold.

Sorting takes $O(m\log(m+1))$ comparisons and finding a minimum load by scanning $n$ bundles per assignment takes $O(nm)$ operations. Loads are sums of at most $m$ rational weights, so encodings stay polynomial. From an explicit matrix known to be proportional, one can take any agent's row as the weights; positive scaling leaves both the ordering and EFX unchanged. The all-zero case can be allocated arbitrarily. ∎

Theorem (Two-agent exact algorithm): For two agents with arbitrary nonnegative rational additive valuations, a complete EFX allocation is computable with a strongly polynomial number of arithmetic operations and polynomial-size encodings.
Proof: Agent 1 partitions the goods into two bundles using the preceding decreasing-weight least-load algorithm with weights $w_g=v_{1g}$. The proof of Theorem Theorem (A strongly polynomial proportional-valuation construction), applied to two copies of that row, shows that either assignment of these bundles is EFX from agent 1's perspective. Agent 2 receives a bundle of weakly greater value according to $v_2$, and agent 1 receives the other. Agent 1's EFX comparisons remain valid by the cutter's property. Agent 2 does not envy agent 1 even before a good is removed, and nonnegative values imply that removal cannot increase the other bundle's value. Hence agent 2 is EFX as well. Sorting, scheduling, and comparing the two bundle values use $O(m\log(m+1))$ operations with polynomial-size rational sums. ∎

Explicit counterexample to an EF1-based proof strategy

Consider two agents and three goods, with rows \[ v_1=(10,1,2),\qquad v_2=(100,2,1). \] Round robin starting with agent 1 gives agent 1 good 1, agent 2 good 2, and agent 1 good 3. Thus $A_1=\{1,3\}$ and $A_2=\{2\}$. Agent 2 values its own bundle at 2. Removing good 3, which it values positively, from $A_1$ leaves value 100, so the EFX inequality fails. Removing good 1 instead leaves value 1, which is at most 2. Agent 1 already prefers its own bundle. Thus this allocation is EF1 (envy can be removed by some good) but not EFX (envy must be removed by every positively valued good). It is not a nonexistence counterexample: Theorem Theorem (Two-agent exact algorithm) supplies an EFX allocation for the same instance.
Proposition (A real counterexample would yield positive integer data): For fixed finite $n,m$, if some real nonnegative additive valuation matrix admits no complete positive-good EFX allocation, then a strictly positive integer valuation matrix of the same dimensions also admits none.
Proof: There are $n^m$ allocations. For each allocation $A$, non-EFX supplies indices $i,j$ and $g\in A_j$ with \[ v_{ig}>0,\qquad D_{A,i,j,g}(v):= v_i(A_j\setminus\{g\})-v_i(A_i)>0. \] Choose one witness for each allocation. There are finitely many chosen strict inequalities, each depending continuously and linearly on the matrix entries. They all remain strict on a sufficiently small neighborhood of the given matrix. Adding a sufficiently small positive number to every entry produces a strictly positive matrix in that neighborhood. Approximate it closely enough by a strictly positive rational matrix that all chosen inequalities still hold. Each allocation retains its original selected violation, so none is EFX. Multiply every entry by a common positive integer denominator. This preserves all strict inequalities and produces the required positive integer matrix. ∎
Corollary (Exact finite linear-arithmetic counterexample search): For fixed $n,m$, existence of any non-EFX instance is equivalent to feasibility of the following disjunctive linear system in the entries of $v$: \[ v_{ig}\ge1\quad(i,g),\qquad \bigwedge_{A\text{ complete}} \bigvee_{\substack{i,j\in[n],\ g\in A_j}} \left[D_{A,i,j,g}(v)\ge1\right]. \] The disjunction for an empty set of potential witnesses is false.
Proof: Every solution has all values positive and a strict EFX violation for each allocation. Conversely, Proposition Proposition (A real counterexample would yield positive integer data) gives a positive integer counterexample whenever any counterexample exists. For each allocation its strictly positive integer violation is at least one, and every entry is at least one. Hence the system is feasible. Equivalently, one can scale the finitely many positive entries and selected positive margins of a real positive counterexample by a common sufficiently large factor. ∎
This formula has one disjunctive clause for every allocation. It is a complete search specification, not a polynomial-size algorithm when $n,m$ vary. Choosing one witness in every clause reduces it to finitely many rational linear-feasibility problems, but the number of choices can be enormous. Restricting to one bundle-size pattern would not test all allocations and would not certify nonexistence.

VERIFICATION

Executed finite searches. All $2^{12}=4,096$ matrices for $n=3,m=4$ with entries in $\{1,2\}$ were tested by exact integer allocation enumeration, and every matrix had an EFX witness. In addition, 1,000 generated positive integer matrices with $n=4,m=6$ and entries in $\{1,\ldots,50\}$ had EFX witnesses. These are finite tests, not a proof for all matrices or all dimensions. The round-robin violation was checked by the same verifier.

For reproducibility, the bundled checks/fairness_check.cpp enumerates allocation labels with good 1 as the least significant base-$n$ digit. It uses integer sums only. Generated matrices use unsigned 32-bit state initialized to 20261009, update $s\leftarrow1664525s+1013904223\pmod{2^{32}}$, and entry $1+(s\bmod50)$, in row-major order. The 1,000 goods instances are generated before the separate chores batch. The following smaller standalone checker reproduces the exhaustive goods grid: 

\begin{lstlisting}[language=Python]
from itertools import product

def efx_goods(v,a): n,m=len(v),len(a) bundles=[[g for g in range(m) if a[g]==i] for i in range(n)] for i in range(n): own=sum(v[i][g] for g in bundles[i]) for j in range(n): positive=[v[i][g] for g in bundles[j] if v[i][g]>0] if positive and own < sum(v[i][g] for g in bundles[j])-min(positive): return False return True

allocations=list(product(range(3),repeat=4)) checked=0 for flat in product((1,2),repeat=12): v=[flat[4*i:4*i+4] for i in range(3)] assert any(efx_goods(v,a) for a in allocations) checked+=1 assert checked==4096 assert not efx_goods(((10,1,2),(100,2,1)),(0,1,0)) print('4096 positive goods matrices and round-robin failure verified') 

\end{lstlisting}

Adversarial audit. The worst removal uses the smallest positive value, not an arbitrary smallest entry. The common-weight proof handles zero-valued goods after positive assignments. The chooser's argument needs nonnegative values. The perturbation proof preserves a chosen strict violation for every allocation simultaneously; perturbing a single allocation would not suffice. No computational search result is extrapolated beyond its specified grid or sample. A deterministic exhaustive algorithm takes $n^m\operatorname{poly}(L)$ time to find a witness or report none; this does not meet the polynomial target.

FINAL

LABEL: UNRESOLVED.

Strongest fully proved partial result. Complete exact EFX allocations have strongly polynomial arithmetic constructions for arbitrary two-agent instances and for proportional valuations with any number of agents. Verification is polynomial. Any real nonexistence example can be converted to a strictly positive integer one of the same dimensions, and an exact finite counterexample-search formula is given. The executed finite searches found no nonexistence example.

Exact first gap. No construction proves complete EFX existence for all nonnegative additive rational instances with arbitrary $n$, and no matrix with no complete EFX allocation is produced. Even a general existence argument would still require a polynomial bit-time search bound to solve the literal algorithmic question.

Three next targets. First, test the disjunctive linear system in dimensions beyond established existence classes while retaining every allocation pattern. Second, seek an invariant for adding one good without leaving charity or weakening EFX; a proof must cover heterogeneous agent rankings, unlike the least-load argument. Third, if an existence process is found, prove a polynomial bound on its number of reassignments rather than using finite termination alone.

Minimal-counterexample information. The elementary proofs exclude $n\le2$, $m\le n$, and proportional valuations. The cited three-agent existence theorem additionally excludes $n=3$; that external theorem is not independently reproved here. A putative nonexistence example can be taken to have strictly positive integer entries and heterogeneous valuation rows. A counterexample to polynomial-time search could instead be a complexity obstruction even when allocations exist; a slow round-robin repair rule would not establish it.

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Econ-23-A1 · Exploration · LLM-generated · gpt 6 astra ultra ·

GPT 6 Astra Ultra attempt on Econ-23

Exact EFX extension tests and a rigorously blocked greedy route

Problem D63-22. Status: unresolved. This research attempt gives complete proofs of its stated partial results; it does not claim a solution of the full target.

Recap and current scope

For binary rational nonnegative additive values $v_i$, the target asks for a complete allocation $A$ in polynomial bit time satisfying \[ v_i(A_i)\geq v_i(A_j\setminus\{g\}) \quad\text{whenever }g\in A_j\text{ and }v_i(g)>0. \] It includes universal existence. Lin, Wu, and Zhou [lin] retain general additive existence as unresolved while studying bi-valued goods. The limited-information approximation paper [limited] concerns a different accuracy/access model and is not a proof of exact polynomial search.

We attack the possibility of completing a partial EFX allocation by adding one item at a time. The result is a complete local criterion and a simple obstruction to using that criterion without rearrangements. A common valuation algorithm and a quantitative perturbation certificate delimit a class where exact construction does work. The identical-valuation existence phenomenon is known [plaut]; no priority claim is made for it.

A complete one-item extension criterion

For a bundle $B$ define \[ E_i(B)=\begin{cases} v_i(B)-\min\{v_i(h):h\in B,\ v_i(h)>0\},&v_i(B)>0,\\ 0,&v_i(B)=0. \end{cases} \] Nonnegativity ensures that the zero case imposes only a vacuous inequality. For all bundles, $v_i(A_i)\geq E_i(A_j)$ is exactly the specified EFX test.
Lemma (Necessary and sufficient safe extension): Let $A$ be a partial EFX allocation and let $g$ be unallocated. Give $g$ to agent $k$, changing no other assignment. The result is EFX if and only if \[ v_i(A_i)\geq E_i(A_k\cup\{g\})\quad\text{for every }i\ne k. \] If $v_i(g)>0$, the right side equals $v_i(A_k)+v_i(g)-\min\{v_i(g),\min_{h\in A_k:v_i(h)>0}v_i(h)\}$, where the inner minimum over an empty set is $+\infty$. If $v_i(g)=0$, that agent imposes no new constraint beyond existing EFX. All recipient candidates can be tested in polynomial bit time.
Proof: Comparisons between bundles other than $A_k$ stay unchanged. Agent $k$'s own value weakly increases, so her comparisons toward other bundles remain valid. Her comparison toward herself holds automatically by nonnegativity. Only the displayed incoming comparisons remain. For a nonzero bundle value, the largest value after removing a positive-valued item is achieved by removing one of minimum positive value, proving the formula. Adding an item of value zero changes neither the bundle's total value nor its set of positive values. Totals and minimum positive singleton values require polynomially many exact rational arithmetic operations with polynomial encodings. ∎
Proposition (Appending alone can stop with one item left): There is a strictly positive additive two-agent, three-good instance with a partial EFX allocation that admits no one-item extension, although the instance has a complete EFX allocation.
Proof: Take goods $a,b,g$ with rows \[ (v_1(a),v_1(b),v_1(g))=(1,2,10),\qquad (v_2(a),v_2(b),v_2(g))=(2,1,10). \] Allocate $a$ to agent 1 and $b$ to agent 2. Both bundles are singletons, so removing their item leaves zero and the partial allocation is EFX. If $g$ goes to agent 1, agent 2 values her own bundle at $1$ but the other bundle with $a$ removed at $10$. If $g$ goes to agent 2, the symmetric comparison violates EFX for agent 1. These are all extension choices. Nevertheless $(\{g\},\{a,b\})$ is EFX: agent 1 has value $10$, which exceeds either remaining singleton value of agent 2's bundle, and removing the only item from agent 1's bundle leaves zero for agent 2 to compare with her own positive value $3$. ∎

A constructive subcase and an exact robustness certificate

Theorem (Common values up to positive row scaling): Suppose $v_i(g)=a_iw_g$ for positive $a_i$ and nonnegative rational $w_g$. Sort the positive-weight goods in nonincreasing $w_g$, and successively give each to a bundle currently having minimum total $w$-weight. Distribute the zero-weight goods arbitrarily. The resulting complete allocation is EFX and is computable in polynomial bit time.
Proof: Fix a nonempty positive-weight bundle $A_j$ and let $h$ be the last positive-weight good assigned to it. It has minimum positive weight in that bundle because of the sorting order. Immediately before assigning $h$, the weight of $A_j\setminus\{h\}$ was at most the then-current weight of every other bundle, hence at most its final weight. Removing any positive item $g$ from $A_j$ leaves no larger weight than removing $h$. Thus $w(A_i)\geq w(A_j\setminus\{g\})$ for all $i,j$ and all positive $g$. Multiplication by $a_i$ gives the required subjective inequalities. Zero-weight items change no comparison and are excluded from the tested removal set. Sorting and maintaining bundle sums use polynomial operations; rational sums have polynomial bit length. The all-zero instance satisfies EFX vacuously. ∎
Proposition (An open neighborhood certified by margins): Let $w_g>0$, $0\leq\epsilon<1$, and suppose positive scalars $a_i$ satisfy \[ (1-\epsilon)a_iw_g\leq v_i(g)\leq(1+\epsilon)a_iw_g \quad\text{for every }i,g. \] For any complete allocation $A$, if \[ (1-\epsilon)w(A_i)\geq(1+\epsilon)w(A_j\setminus\{g\}) \quad\text{for all }i\ne j,\ g\in A_j, \] then $A$ is exact EFX for $v$. In particular, if all these common-weight inequalities are strict at $\epsilon=0$, the same allocation is EFX throughout some positive-radius multiplicative neighborhood of the scaled common values.
Proof: Add the lower singleton bounds over $A_i$ and the upper bounds over $A_j\setminus\{g\}$, and use the assumed weight inequality. This gives the actual EFX inequality for $i\ne j$; the $i=j$ condition follows directly from nonnegative values. For the neighborhood conclusion, each comparison has the form $(1-\epsilon)b\geq(1+\epsilon)c$ with $b>c\geq0$. It holds whenever $\epsilon\leq(b-c)/(b+c)$. There are finitely many tests, and all these ratios are positive. Their minimum, reduced if necessary to less than one, supplies a positive common radius. With no tests the conclusion is vacuous and any radius below one suffices. ∎
Corollary (Balanced near-uniform goods): Let $m=nk$ with $k\geq1$. If every row has all its singleton values in $[a_i,\frac{k}{k-1}a_i]$ for $a_i>0$ and $k\geq2$, then every allocation with $k$ goods per agent is EFX. For $k=1$, every singleton allocation is EFX without any restriction on positive value ratios.
Proof: For $k\geq2$, every own bundle has value at least $ka_i$, and every other bundle after a removal has at most $(k-1)\frac{k}{k-1}a_i=ka_i$. For $k=1$ all tested residual bundles are empty. ∎

Verification and the first gap

The extension obstruction is verified by its two possible placements and by all comparisons in the displayed complete allocation. It does not disprove EFX existence. The common-value proof explicitly handles zero values using the problem's positive-removal convention. For unrestricted inputs the local criterion need not admit a recipient, and no polynomial sequence of global rearrangements is supplied to repair such blocked states. That is the first gap in this proposed approach; the universal exact-search target remains unresolved.
Finite implementation checks.
Exact integer checks covered all placements in the obstruction and its complete repair. The common-value algorithm was also checked on 100 positive integer-weight instances with three agents and proportional cost/value rows, using random seed $20261009$; both the goods and chore comparison directions passed. These finite checks do not establish unrestricted existence.

References

  • [lin] Z. Lin, X. Wu, and S. Zhou, When Maximum Nash Welfare Becomes Strongly Fair: Bi-valued Goods, arXiv:2610.00122v1 (2026), Section 1.2. https://arxiv.org/html/2610.00122v1.
  • [limited] A. Filos-Ratsikas, G. Kalantzis, and A. A. Voudouris, Approximate-EFX Allocations with Ordinal and Limited Cardinal Information, arXiv:2602.08714v1 (2026). https://arxiv.org/abs/2602.08714v1.
  • [plaut] B. Plaut and T. Roughgarden, Almost Envy-Freeness with General Valuations, SIAM Journal on Discrete Mathematics 34(2), 1039--1068 (2020). https://doi.org/10.1137/19M124397X.

Completion Estimate

15%

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